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Identify which instrument is string or percussion: Atumpan, Axatse, Baryton
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Atumpan and Axatse are percussion, Baryton is string.
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Since the conclusion of the War of 1812, historians have long debated the relative weight of the multiple reasons underlying its origins.
During the nineteenth century, historians generally concluded that war was declared largely over national honour, neutral maritime rights and the British seizure of neutral ships and their cargoes on the high seas. This theme was the basis of President James Madison's war message to Congress on June 1, 1812. At the turn of the 20th century, much of the contemporary scholarship re-evaluated this explanation and began to focus more on non-maritime factors as significant contributing causes as well. However, historian Warren H. Goodman warns that too much focus on these ideas can be equally misleading.
In disagreeing with those interpretations that have simply stressed expansionism and minimized maritime causation, historians have ignored deep-seated American fears for national security, dreams of a continent completely controlled by the republican United States, and the evidence that many Americans believed that the War of 1812 would be the occasion for the United States to achieve the long-desired annexation of Canada. [...] Thomas Jefferson well summarized American majority opinion about the war [...] to say "that the cession of Canada [...] must be a sine qua non at a treaty of peace." - Horsman
Historian Richard Maass argues that the expansionist theme is a myth that goes against the "relative consensus among experts that the primary American objective was the repeal of British maritime restrictions". He says that scholars agree that the United States went to war "because six years of economic sanctions had failed to bring Britain to the negotiating table, and threatening the Royal Navy's Canadian supply base was their last hope". Maass agrees that expansionism might have tempted Americans on a theoretical level, but he finds that "leaders feared the domestic political consequences of doing so", particularly because such expansion "focused on sparsely populated western lands rather than the more populous eastern settlements". To what extent that American leaders considered the question of pursuing territory in Canada, those questions "arose as a result of the war rather than as a driving cause." However, Maass accepts that many historians continue to believe that expansionism was a cause.
Reginald Horsman sees expansionism as a secondary cause after maritime issues, noting that many historians have mistakenly rejected expansionism as a cause for the war. He notes that it was considered key to maintaining sectional balance between free and slave states thrown off by American settlement of the Louisiana Territory and widely supported by dozens of War Hawk congressmen such as Henry Clay, Felix Grundy, John Adams Harper and Richard Mentor Johnson, who voted for war with expansion as a key aim. However, Horsman states that in his view "the desire for Canada did not cause the War of 1812" and that "The United States did not declare war because it wanted to obtain Canada, but the acquisition of Canada was viewed as a major collateral benefit of the conflict".
However, other historians believe that a desire to permanently annex Canada was a direct cause of the war.[full citation needed] Carl Benn notes that the War Hawks' desire to annex the Canadas was similar to the enthusiasm for the annexation of Spanish Florida by inhabitants of the American South as both expected war to facilitate expansion into long-desired lands and end support for hostile tribes (Tecumseh's Confederacy in the North and the Creek in the South).
Alan Taylor says that many Democratic-Republican congressmen such as John Adams Harper, Richard Mentor Johnson and Peter Buell Porter "longed to oust the British from the continent and to annex Canada". A few Southerners opposed this, fearing an imbalance of free and slave states if Canada was annexed. Anti-Catholicism also caused many to oppose annexing the mainly Catholic Lower Canada, believing its French-speaking inhabitants unfit "for republican citizenship".
Even major figures such as Henry Clay and James Monroe expected to keep at least Upper Canada in an easy conquest. Notable American generals such as William Hull issued proclamations to Canadians during the war promising republican liberation through incorporation into the United States. General Alexander Smyth similarly declared to his troops when they invaded Canada that "you will enter a country that is to become one of the United States. You will arrive among a people who are to become your fellow-citizens". However, a lack of clarity about American intentions undercut these appeals.
David and Jeanne Heidler argue that "most historians agree that the War of 1812 was not caused by expansionism but instead reflected a real concern of American patriots to defend United States' neutral rights from the overbearing tyranny of the British Navy. That is not to say that expansionist aims would not potentially result from the war". However, they also argue otherwise, saying that "acquiring Canada would satisfy America's expansionist desires", also describing it as a key goal of western expansionists who, they argue, believed that "eliminating the British presence in Canada would best accomplish" their goal of halting British support for tribal raids. They argue that the "enduring debate" is over the relative importance of expansionism as a factor, and whether "expansionism played a greater role in causing the War of 1812 than American concern about protecting neutral maritime rights".
In the 1960s, the work of Norman K. Risjord, Reginald Horsman, Bradford Perkins and Roger Brown established a new eastern maritime consensus. While these authors approached the origins of the war from many perspectives, they all conceded that British maritime policy was the principal cause of the war.
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Provide a short, bulleted summary of what historians consider the cause of the War of 1812
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Different historians believe the war of 1812 was caused by:
1. National honor, neutral maritime rights, and naval ship seizures
2. Desire by the United States to permanently annex Canada
3. British maritime policy and its perceived threat on the United States
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A season is a division of the year marked by changes in weather, ecology, and the amount of daylight. The growing season is that portion of the year in which local conditions (i.e. rainfall, temperature, daylight) permit normal plant growth. While each plant or crop has a specific growing season that depends on its genetic adaptation, growing seasons can generally be grouped into macro-environmental classes.
|
What is Growing Season?
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Season extension in agriculture is any method that allows a crop to be grown beyond its normal outdoor growing season and harvesting time frame, or the extra time thus achieved.
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null | false
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Classify the cities based on whether they are located in Namibia or Lithuania.
Klaipėda, Oshakati, Kaunas, Windhoek, Vilnius, Rundu
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Namibia - Windhoek, Rundu, Oshakati
Lithuania - Vilnius, Kaunas, Klaipėda
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null | false
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Which onsen villages in Japan are the best to visit for foreign tourists? And what should I do once I'm in an onsen village?
|
There are many great onsen villages in Japan in which tourists of any nationals can enjoy the experience of bathing in natural hot springs. Some great examples are Hakone, Kusatsu and Nyuto Onsen. Ideally, you would want to spend a day or two in a village as these villages are often located in beautiful nature and offer great opportunities for hiking and walking.
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1805.00760
| true
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Ablation Study
To further investigate the efficacy of the key components in our framework, namely, THA and STN, we perform ablation study as shown in the second block of Table TABREF39 . The results show that each of THA and STN is helpful for improving the performance, and the contribution of STN is slightly larger than THA. “OURS w/o THA & STN” only keeps the basic bi-linear attention. Although it performs not bad, it is still less competitive compared with the strongest baseline (i.e., CMLA), suggesting that only using attention mechanism to distill opinion summary is not enough. After inserting the STN component before the bi-linear attention, i.e. “OURS w/o THA”, we get about 1% absolute gains on each dataset, and then the performance is comparable to CMLA. By adding THA, i.e. “OURS”, the performance is further improved, and all state-of-the-art methods are surpassed.
Ablation Study
To further investigate the efficacy of the key components in our framework, namely, THA and STN, we perform ablation study as shown in the second block of Table TABREF39 . The results show that each of THA and STN is helpful for improving the performance, and the contribution of STN is slightly larger than THA. “OURS w/o THA & STN” only keeps the basic bi-linear attention. Although it performs not bad, it is still less competitive compared with the strongest baseline (i.e., CMLA), suggesting that only using attention mechanism to distill opinion summary is not enough. After inserting the STN component before the bi-linear attention, i.e. “OURS w/o THA”, we get about 1% absolute gains on each dataset, and then the performance is comparable to CMLA. By adding THA, i.e. “OURS”, the performance is further improved, and all state-of-the-art methods are surpassed.
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Do they explore how useful is the detection history and opinion summary?
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Yes.
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1912.02481
| false
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As a first experiment, we compare the quality of fastText embeddings trained on (high-quality) curated data and (low-quality) massively extracted data for Twi and Yorùbá languages.
The huge ambiguity in the written Twi language motivates the exploration of different approaches to word embedding estimations. In this work, we compare the standard fastText methodology to include sub-word information with the character-enhanced approach with position-based clustered embeddings (CWE-LP as introduced in Section SECREF17). With the latter, we expect to specifically address the ambiguity present in a language that does not translate the different oral tones on vowels into the written language.
As a first experiment, we compare the quality of fastText embeddings trained on (high-quality) curated data and (low-quality) massively extracted data for Twi and Yorùbá languages.
The huge ambiguity in the written Twi language motivates the exploration of different approaches to word embedding estimations. In this work, we compare the standard fastText methodology to include sub-word information with the character-enhanced approach with position-based clustered embeddings (CWE-LP as introduced in Section SECREF17).
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What two architectures are used?
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The answers are shown as follows:
* fastText
* CWE-LP
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null | false
| 204
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As explained in the introduction, the overarching aim of this project is to investigate in how far information conveyed in social media posts can complement more traditional research methods in clinical psychology to get insights into the recovery experience of individuals with a BD diagnosis. Therefore, we will first conduct a systematic literature review of qualitative evidence to establish a solid base of what is already known about personal recovery experiences in BD for the subsequent social media studies.
Our research questions, which regard the experiences of different populations, lend themselves to several subprojects. First, we will collect and analyse English-language data from westerners. Then, we will address ethnically diverse English-speaking populations and finally multilingual accounts. This has the advantage that we can build data processing and methodological workflows along an increase in complexity of the data collection and analysis throughout the project.
In each project phase, we will employ a mixed-methods approach to combine the advantages of quantitative and qualitative methods BIBREF52 , BIBREF53 , which is established in mental health research BIBREF54 , BIBREF55 , BIBREF56 , BIBREF57 and specifically recommended to investigate personal recovery BIBREF58 . Quantitative methods are suitable to study observable behaviour such as language and yield more generalisable results by taking into account large samples. However, they fall short of capturing the subjective, idiosyncratic meaning of socially constructed reality, which is important when studying individuals' recovery experience BIBREF59 , BIBREF22 , BIBREF23 , BIBREF60 . Therefore, we will apply an explanatory sequential research design BIBREF53 , starting with statistical analysis of the full dataset followed by a manual investigation of fewer examples, similar to `distant reading' BIBREF61 in digital humanities.
Since previous research mainly employed (semi-)structured interviews and we do not expect to necessarily find the same aspects emphasised in unstructured settings, even less so when looking at a more diverse and non-English speaking population, we will not derive hypotheses from existing recovery models for testing on the online data. Instead, we will start off with exploratory quantitative research using comparative analysis tools such as Wmatrix BIBREF62 to uncover important linguistic features, e.g., on keywords and key concepts that occur with unexpected frequency in our collected datasets relative to reference corpora. The underlying assumption is that keywords and key concepts are indicative of certain aspects of personal recovery, such as those specified in the CHIME model BIBREF24 , other previous research BIBREF22 , BIBREF23 , BIBREF60 , or novel ones. Comparing online sources with transcripts of structured interviews or subcorpora originating from different cultural backgrounds might uncover aspects that were not prominently represented in the accounts studied in prior research.
A specific challenge will be to narrow down the data to parts relevant for personal recovery, since there is no control over the discussed topics compared to structured interviews. To investigate how individuals discuss personal recovery online and what (potentially unrecorded) aspects they associate with it, without a priori narrowing down the search-space to specific known keywords seems like a chicken-and-egg problem. We propose to address this challenge by an iterative approach similar to the one taken in a corpus linguistic study of cancer metaphors BIBREF63 . Drawing on results from previous qualitative research BIBREF24 , BIBREF23 , we will compile an initial dictionary of recovery-related terms. Next, we will examine a small portion of the dataset manually, which will be partly randomly sampled and partly selected to contain recovery-related terms. Based on this, we will be able to expand the dictionary and additionally automatically annotate semantic concepts of the identified relevant text passages using a semantic tagging approach such as the UCREL Semantic Analysis System (USAS) BIBREF64 . Crucially for the multilingual aspect of the project, USAS can tag semantic categories in eight languages BIBREF8 . Then, semantic tagging will be applied to the full corpus to retrieve all text passages mentioning relevant concepts. Furthermore, distributional semantics methods BIBREF65 , BIBREF66 can be used to find terms that frequently co-occur with words from our keyword dictionary. Occurrences of the identified keywords or concepts can be quantified in the full corpus to identify the importance of the related personal recovery aspects.
Linguistic Inquiry and Word Count (LIWC) BIBREF67 is a frequently used tool in social-science text analysis to analyse emotional and cognitive components of texts and derive features for classification models BIBREF47 , BIBREF46 , BIBREF68 , BIBREF69 . LIWC counts target words organised in a manually constructed hierarchical dictionary without contextual disambiguation in the texts under analysis and has been psychometrically validated and developed for English exclusively. While translations for several languages exist, e.g., Dutch BIBREF9 , and it is questionable to what extent LIWC concepts can be transferred to other languages and cultures by mere translation. We therefore aim to apply and develop methods that require less manual labour and are applicable to many languages and cultures. One option constitute unsupervised methods, such as topic modelling, which has been applied to explore cultural differences in mental-health related online data already BIBREF37 , BIBREF36 . The Differential Language Analysis ToolKit (DLATK) BIBREF70 facilitates social-scientific language analyses, including tools for preprocessing, such as emoticon-aware tokenisers, filtering according to meta data, and analysis, e.g. via robust topic modelling methods.
Furthermore, emotion and sentiment analysis constitute useful tools to investigate the emotions involved in talking about recovery and identify factors that facilitate or hinder it. There are many annotated datasets to train supervised classifiers BIBREF71 , BIBREF3 for these actively researched NLP tasks. Machine learning methods were found to usually outperform rule-based approaches based on look-ups in dictionaries such as LIWC. Again, most annotated resources are English, but state of the art approaches based on multilingual embeddings allow transferring models between languages BIBREF4 .
Machine learning methods were found to usually outperform rule-based approaches based on look-ups in dictionaries such as LIWC.
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Do the machine learning methods outperform rule-based approaches based on look-ups in dictionaries such as LIWC?
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Yes, they do.
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The 1958 North Dakota State Bison football team was an American football team that represented North Dakota State University during the 1958 NCAA College Division football season as a member of the North Central Conference. In their second year under head coach Bob Danielson, the team compiled a 3–6 record.
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Did the Bisons have a winning season in 1958?
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No, the Bisons had a losing season in 1958, only winning 3 of their 9 games and losing 6.
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null | false
| null |
What is the NBA All-Star game?
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The NBA All-Star game is a mid-season game where the players who are voted as having the best seasons so far play against each other.
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null | false
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Write me a text to someone I work with asking them to cover my shift because I got sick. Use a polite tone.
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Hi <insert name here>, I’m running a fever and my doctor recommended I find someone else to take my shift. Unfortunately it’s contagious and I’m worried that I’ll get everyone else sick if I go. Is there any chance you could step in for me? I’ll owe you one big time, but totally get it if this is too last minute. Let me know!
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| 219
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To evaluate the session segmentation method, we used a real-world chatting corpus from DuMi, a state-of-the-practice open-domain conversation system in Chinese. We sampled 200 sessions as our experimental corpus. Session segmentation was manually annotated before experiments, serving as the ground truth. The 200 sessions were randomly split by 1:1 for validation and testing. Notice that, our method does not require labeled training samples; massive data with labels of high quality are quite expensive to obtain.
We also leveraged an unlabeled massive dataset of conversation utterances to train our word embeddings with “virtual sentences.” The dataset was crawled from the Douban forum, containing 3 million utterances and approximately 150,000 unique words (Chinese terms).
To evaluate the session segmentation method, we used a realworld chatting corpus from DuMi,3 a state-of-the-practice open-domain conversation system in Chinese. We sampled 200 sessions as our experimental corpus. Session segmentation was manually annotated before experiments, serving as the ground truth. The 200 sessions were randomly split by 1:1 for validation and testing. Notice that, our method does not require labeled training samples; massive data with labels of high quality are quite expensive to obtain. We also leveraged an unlabeled massive dataset of conversation utterances to train our word embeddings with “virtual sentences.” The dataset was crawled from the Douban forum,4 containing 3 million utterances and approximately 150,000 unique words (Chinese terms).
|
What corpus do they use to evaluate the session segmentation method?
|
A real world chatting corpus from DuMi and an unlabeled massive dataset of conversation utterances
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null | false
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Write do's and don'ts for handling a PR crisis. Make them directive and easy for anyone to understand and follow.
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Do:
Assume "worst-case" outcomes for planning purposes.
Inform key audiences regularly and accurately from the outset.
Ensure communications are candid, accurate and timely.
Focus on concrete actions being taken to understand or address the issue.
Retain control of the story. Both positive and negative information needs to come from the company to help assure a transparent posture.
Provide information that's relevant to the public's interest rather than just the company's.
Consider seeking third-party support to help explain the crisis to various audiences.
Explain the situation directly to key audiences using the channels that are familiar to them.
Where appropriate, contact public officials and leaders proactively. Keep them informed throughout the process.
Don't:
Be silent or make “no comment” statements — they are often interpreted as admissions of guilt and lack of preparation.
Make "off-the-record" statements — there is no such thing in a crisis situation.
Speculate on anything or provide incomplete information. Stick to the facts.
Let erroneous information or ill-informed opinions go unchallenged.
Release sensitive or proprietary information.
Try to minimize the problem, but also don’t make it a bigger deal than it is. Match the level of your response to the severity of the issue.
Rely on communications alone to fix a crisis. You have to address the issue that led to the PR crisis in the first place.
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null | false
| 59
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We evaluate Jasper across a number of datasets in various domains. In all experiments, we use dropout and weight decay as regularization. At training time, we use speed perturbation with fixed +/-10% BIBREF29 for LibriSpeech. For WSJ and Hub5'00, we use a random speed perturbation factor between [-10%, 10%] as each utterance is fed into the model. All models have been trained on NVIDIA DGX-1 in mixed precision BIBREF30 using OpenSeq2Seq BIBREF31 . Source code, training configurations, and pretrained models are available.
In all experiments, we use dropout and weight decay as regularization.
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What does the team utilize as regularization in their experiments?
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Dropout and weight decay .
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1910.01363
| false
| null |
For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter. It contains tweets collected based on keyword search that were posted between July 17, 2014 (the day of the plane crash) and December 9, 2016.
For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter.
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What dataset is used for this study?
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The answers are shown as follows:
* MH17 Twitter dataset
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1609.00081
| false
| null |
Results of Predictive Models. For the purpose of evaluation, we report the average results after 10-fold cross-validation. Here we consider five baselines to compare with GraLap: (i) Uniform: assign 3 to all the references assuming equal intensity, (ii) SVR+W: recently proposed Support Vector Regression (SVR) with the feature set mentioned in BIBREF4 , (iii) SVR+O: SVR model with our feature set, (iv) C4.5SSL: C4.5 semi-supervised algorithm with our feature set BIBREF23 , and (v) GLM: the traditional graph-based LP model with our feature set BIBREF9 . Three metrics are used to compare the results of the competing models with the annotated labels: Root Mean Square Error (RMSE), Pearson's correlation coefficient ( INLINEFORM0 ), and coefficient of determination ( INLINEFORM1 ).
Here we consider five baselines to compare with GraLap: (i) Uniform: assign 3 to all the references assuming equal intensity, (ii) SVR+W: recently proposed Support Vector Regression (SVR) with the feature set mentioned in BIBREF4 , (iii) SVR+O: SVR model with our feature set, (iv) C4.5SSL: C4.5 semi-supervised algorithm with our feature set BIBREF23 , and (v) GLM: the traditional graph-based LP model with our feature set BIBREF9 .
|
What are the baselines model?
|
The answers are shown as follows:
* (i) Uniform
* (ii) SVR+W
* (iii) SVR+O
* (iv) C4.5SSL
* (v) GLM
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null | false
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Lollapalooza /ˌlɒləpəˈluːzə/ (Lolla) is an annual American four-day music festival held in Grant Park in Chicago. It originally started as a touring event in 1991, but several years later, Chicago became its permanent location. Music genres include but are not limited to alternative rock, heavy metal, punk rock, hip hop, and electronic dance music. Lollapalooza has also featured visual arts, nonprofit organizations, and political organizations. The festival, held in Grant Park, hosts an estimated 400,000 people each July and sells out annually. Lollapalooza is one of the largest and most iconic music festivals in the world and one of the longest-running in the United States.
Lollapalooza was conceived and created in 1991 as a farewell tour by Perry Farrell, singer of the group Jane's Addiction.
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Given a reference text about Lollapalooza, where does it take place, who started it and what is it?
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Lollapalooze is an annual musical festival held in Grant Park in Chicago, Illinois. It was started in 1991 as a farewell tour by Perry Farrell, singe of the group Jane's Addiction. The festival includes an array of musical genres including alternative rock, heavy metal, punk rock, hip hop, and electronic dance music. The festivals welcomes an estimated 400,000 people each year and sells out annually. Some notable headliners include: the Red Hot Chili Peppers, Chance the Rapper, Metallica, and Lady Gage. Lollapalooza is one of the largest and most iconic festivals in the world and a staple of Chicago.
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null | false
| 185
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We use a pre-trained Xnlg with a 10-layer encoder and a 6-layer decoder. For every Transformer layer, we use 1024 hidden units, 8 attention heads, and GELU activations BIBREF26. In the first pre-training stage, we directly use the 15-language pre-trained XLM BIBREF5 to initialize the parameters of our encoder and decoder. In the second stage, we use Wikipedia as the monolingual data for the DAE objective, and MultiUN BIBREF27 as the parallel data for the XAE objective. The DAE loss is trained with a weight of $0.5$. We train a two-language (English/Chinese) and a three-language (English/French/Chinese) Xnlg for two downstream NLG tasks, respectively. Following BIBREF5, we use the tokenizer provided by BIBREF28 for Chinese, and Moses for other languages, respectively. Then the words in all languages are split with a shared subword vocabulary learned by BPE BIBREF29. We use Adam optimizer with a linear warm-up over the first 4,000 steps and linear decay for later steps, and the learning rate is set to $10^{-4}$. The pre-training batch size is 64, and the sequence length is set to 256. It takes about 30 hours to run 23,000 steps for the pre-training procedure by using 4 Nvidia Telsa V100-16GB GPUs.
For fine-tuning on downstream NLG tasks, we use Adam optimizer with a learning rate of $5\times 10^{-6}$. We set the batch size as 16 and 32 for question generation and abstractive summarization, respectively. When the target language is the same as the language of training data, we fine-tune all parameters. When the target language is different from the language of training data, we fine-tune the Transformer layers of the encoder. We truncate the input sentences to the first 256 tokens. During decoding, we use beam search with beam size of 3, and limit the length of the target sequence to 80 tokens.
We use a pre-trained XNLG with a 10-layer encoder and a 6-layer decoder. For every Transformer layer, we use 1024 hidden units, 8 attention heads, and GELU activations (Hendrycks and Gimpel 2016). In the first pretraining stage, we directly use the 15-language pre-trained XLM (Lample and Conneau 2019) to initialize the parameters of our encoder and decoder
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How many attention heads are there in the transformer layer?
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8 attention heads.
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null | false
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On 17 March 1773, Auguste’s spouse succeeded as the 4th Prince of Thurn and Taxis. The marriage was at that point severely deteriorated. Auguste left Karl Anselm and settled in Paris. Rumours claimed that she lived with a lover. However, she was tormented by economical problems, ran out of funds and was hounded by creditors.
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Given this paragraph about Duchess Auguste of Württemberg, what happened to personal life?
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Auguste’s marriage was severely deteriorated and she had debt problems.
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null | false
| null |
7 factors led to nokia failure
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Nokia total dependency only on hardware
Nokia lack of innovation and accept feedback
Not accepting Android on time and becoming a part of the ecosystem
Rise of the Chinese mobile market as global players
Stephen Elop as the CEO who took a series of bad decision
It could not read the new trend and pushed its arrogance on customer trust
Not incentivising the dealer
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null | false
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Autism therapies include a wide variety of therapies that help people with autism, or their families. Such methods of therapy also seek the increase of functional independence in autistic people. Many therapies marketed towards autistic people and/or their parents claim outcomes that have not been supported by Level of Research (LOE) Level 1 (highest level assigned based on the methodological quality of their design, validity, and applicability to patient care). Level 1 research includes evidence from a systematic review or meta-analysis of all relevant RCTs (randomized controlled trial) or evidence-based clinical practice guidelines based on systematic reviews of RCTs or three or more RCTs of good quality that have similar results.
Autism is a neurotype characterized by sensory and communication difference, when compared to neurotypical and allistic individuals.None of these therapies eliminate autism within someone, let alone to a high degree of viability. Autistic children grow up to become autistic adults at risk of burnout and post-traumatic stress disorder (PTSD) inflicted during childhood and adolescence, often overlooked by those who prioritize the elimination of autism over the common well-being of autistic people. Treatment is typically catered to the person's needs. Treatments fall into two major categories: educational interventions and medical management. Training and support are also given to families of those diagnosed with autism spectrum disorders (ASDs)
Studies of interventions have some methodological problems that prevent definitive conclusions about efficacy.Although many psychosocial interventions have some positive evidence, suggesting that some form of treatment is preferable to no treatment, the systematic reviews have reported that the quality of these studies has generally been poor, their clinical results are mostly tentative, and there is little evidence for the relative effectiveness of treatment options.Intensive, sustained special education programs and behavior therapy early in life can help children with ASD acquire self-care, social, and job skills,and often can improve functioning, and decrease symptom severity and maladaptive behaviors;Available approaches include applied behavior analysis (ABA), developmental models, structured teaching, speech and language therapy, social skills therapy, and occupational therapy. Occupational therapists work with autistic children by creating interventions that promote social interaction like sharing and cooperation.They also support the autistic child by helping them work through a dilemma as the OT imitates the child and waiting for a response from the child.Educational interventions have some effectiveness in children: intensive ABA treatment has demonstrated effectiveness in enhancing global functioning in preschool children,and is well established for improving intellectual performance of young children.Neuropsychological reports are often poorly communicated to educators, resulting in a gap between what a report recommends and what education is provided.The limited research on the effectiveness of adult residential programs shows mixed results.
Many medications are used to treat problems associated with ASD.More than half of U.S. children diagnosed with ASD are prescribed psychoactive drugs or anticonvulsants, with the most common drug classes being antidepressants, stimulants.Aside from antipsychotics,there is scant reliable research about the effectiveness or safety of drug treatments for adolescents and adults with ASD.A person with ASD may respond atypically to medications, the medications can have adverse effects, and no known medication relieves autism's core symptoms of social and communication impairments.
As of 2008 the treatments prescribed to children with ASD were expensive;indirect costs are more so. For someone born in 2000, a U.S. study estimated an average discounted lifetime cost of $4.66 million (2023 dollars, inflation-adjusted from 2003 estimate), with about 10% medical care, 30% extra education and other care, and 60% lost economic productivity.A UK study estimated discounted lifetime costs at £1.9 million and £1.23 million for an autistic person with and without intellectual disability, respectively(2023 pounds, inflation-adjusted from 2005/06 estimate). Legal rights to treatment are complex, vary with location and age, and require advocacy by caregivers. Publicly supported programs are often inadequate or inappropriate for a given child, and unreimbursed out-of-pocket medical or therapy expenses are associated with likelihood of family financial problems;one 2008 U.S. study found a 14% average loss of annual income in families of children with ASD, and a related study found that ASD is associated with higher probability that child care problems will greatly affect parental employment.
|
What therapies are available for Autism?
|
There are multiple therapies available for Autism depending on the spectrum.
Intensive, sustained special education programs and behaviour therapy early in life can help children with ASD acquire self-care, social, and job skills, and often can improve functioning and decrease symptom severity and maladaptive behaviours; Available approaches include applied behaviour analysis (ABA), developmental models, structured teaching, speech and language therapy, social skills therapy, and occupational therapy. Occupational therapists work with autistic children by creating interventions that promote social interaction like sharing and cooperation. They also support the autistic child by helping them work through a dilemma as the OT imitates the child and waits for a response from the child. Educational interventions have some effectiveness in children: intensive ABA treatment has demonstrated effectiveness in enhancing global functioning in preschool children and is well-established for improving the intellectual performance of young children.
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null | false
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Who is the first African-American to perform at the Metropolitan Opera?
|
Marian Anderson was the first African-American to perform at the Metropolitan Opera.
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2004.02393
| false
| null |
Method ::: Passage Ranking Model
The key component of our framework is the Ranker model, which is provided with a question $q$ and $K$ passages $\mathcal {P} = \lbrace p_1, p_2 ... p_K\rbrace $ from a pool of candidates, and outputs a chain of selected passages.
Method ::: Cooperative Reasoner
To alleviate the noise in the distant supervision signal $\mathcal {C}$, in addition to the conditional selection, we further propose a cooperative Reasoner model, also implemented with the MatchLSTM architecture (see Appendix SECREF6), to predict the linking entity from the selected passages. Intuitively, when the Ranker makes more accurate passage selections, the Reasoner will work with less noisy data and thus is easier to succeed. Specifically, the Reasoner learns to extract the linking entity from chains selected by a well-trained Ranker, and it benefits the Ranker training by providing extra rewards. Taking 2-hop as an example, we train the Ranker and Reasoner alternatively as a cooperative game:
Method ::: Passage Ranking Model
The key component of our framework is the Ranker model, which is provided with a question $q$ and $K$ passages $\mathcal {P} = \lbrace p_1, p_2 ... p_K\rbrace $ from a pool of candidates, and outputs a chain of selected passages.
Method ::: Cooperative Reasoner
To alleviate the noise in the distant supervision signal $\mathcal {C}$, in addition to the conditional selection, we further propose a cooperative Reasoner model, also implemented with the MatchLSTM architecture (see Appendix SECREF6), to predict the linking entity from the selected passages.
|
What are two models' architectures in proposed solution?
|
The answers are shown as follows:
* Reasoner model, also implemented with the MatchLSTM architecture
* Ranker model
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| 154
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All corpora provide datasets/splits for answer selection, whereas only (WikiQA, SQuAD) and (WikiQA, SelQA) provide datasets for answer extraction and answer triggering, respectively. SQuAD is much larger in size although questions in this corpus are often paraphrased multiple times. On the contrary, SQuAD's average candidates per question ( INLINEFORM0 ) is the smallest because SQuAD extracts answer candidates from paragraphs whereas the others extract them from sections or infoboxes that consist of bigger contexts. Although InfoboxQA is larger than WikiQA or SelQA, the number of token types ( INLINEFORM1 ) in InfoboxQA is smaller than those two, due to the repetitive nature of infoboxes.
All corpora show similar average answer candidate lengths ( INLINEFORM0 ), except for InfoboxQA where each line in the infobox is considered a candidate. SelQA and SQuAD show similar average question lengths ( INLINEFORM1 ) because of the similarity between their annotation schemes. It is not surprising that WikiQA's average question length is the smallest, considering their questions are taken from search queries. InfoboxQA's average question length is relatively small, due to the restricted information that can be asked from the infoboxes. InfoboxQA and WikiQA show the least question-answer word overlaps over questions and answers ( INLINEFORM2 and INLINEFORM3 in Table TABREF2 ), respectively. In terms of the F1-score for overlapping words ( INLINEFORM4 ), SQuAD gives the least portion of overlaps between question-answer pairs although WikiQA comes very close.
Fig. FIGREF4 shows the distributions of seven question types grouped deterministically from the lexicons. Although these corpora have been independently developed, a general trend is found, where the what question type dominates, followed by how and who, followed by when and where, and so on.
Fig. FIGREF6 shows the distributions of answer categories automatically classified by our Convolutional Neural Network model trained on the data distributed by li:02a. Interestingly, each corpus focuses on different categories, Numeric for WikiQA and SelQA, Entity for SQuAD, and Person for InfoboxQA, which gives enough diversities for statistical learning to build robust models.
All corpora provide datasets/splits for answer selection, whereas only (WIKIQA, SQUAD) and (WIKIQA, SELQA) provide datasets for answer extraction and answer triggering, respectively.
|
Do all corpora provide datasets/splits for answer selection?
|
Yes, they do.
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| 80
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Automatic text summarization has been an active research area in natural language processing for several decades. To compare and evaluate the performance of different summarization systems, the most intuitive approach is assessing the quality of the summaries by human evaluators. However, manual evaluation is expensive and the obtained results are subjective and difficult to reproduce BIBREF0 . To address these problems, automatic evaluation measures for summarization have been proposed. Rouge BIBREF1 is one of the first and most widely used metrics in summarization evaluation. It facilitates evaluation of system generated summaries by comparing them to a set of human written gold-standard summaries. It is inspired by the success of a similar metric Bleu BIBREF2 which is being used in Machine Translation (MT) evaluation. The main success of Rouge is due to its high correlation with human assessment scores on standard benchmarks BIBREF1 . Rouge has been used as one of the main evaluation metrics in later summarization benchmarks such as TAC[1] BIBREF3 .
[1]Text Analysis Conference (TAC) is a series of workshops for evaluating research in Natural Language Processing
Since the establishment of Rouge, almost all research in text summarization have used this metric as the main means for evaluating the quality of the proposed approaches. The public availability of Rouge as a toolkit for summarization evaluation has contributed to its wide usage. While Rouge has originally shown good correlations with human assessments, the study of its effectiveness was only limited to a few benchmarks on news summarization data (DUC[2] 2001-2003 benchmarks). Since 2003, summarization has grown to much further domains and genres such as scientific documents, social media and question answering. While there is not enough compelling evidence about the effectiveness of Rouge on these other summarization tasks, published research is almost always evaluated by Rouge. In addition, Rouge has a large number of possible variants and the published research often (arbitrarily) reports only a few of these variants.
[2]Document Understanding Conference (DUC) was one of NIST workshops that provided infrastructure for evaluation of text summarization methodologies (http://duc.nist.gov/).
By definition, Rouge solely relies on lexical overlaps (such as n-gram and sequence overlaps) between the system generated and human written gold-standard summaries. Higher lexical overlaps between the two show that the system generated summary is of higher quality. Therefore, in cases of terminology nuances and paraphrasing, Rouge is not accurate in estimating the quality of the summary.
We study the effectiveness of Rouge for evaluating scientific summarization. Scientific summarization targets much more technical and focused domains in which the goal is providing summaries for scientific articles. Scientific articles are much different than news articles in elements such as length, complexity and structure. Thus, effective summarization approaches usually have much higher compression rate, terminology variations and paraphrasing BIBREF4 .
Scientific summarization has attracted more attention recently (examples include works by abu2011coherent, qazvinian2013generating, and cohan2015scientific). Thus, it is important to study the validity of existing methodologies applied to the evaluation of news article summarization for this task. In particular, we raise the important question of how effective is Rouge, as an evaluation metric for scientific summarization? We answer this question by comparing Rouge scores with semi-manual evaluation score (Pyramid) in TAC 2014 scientific summarization dataset[1]. Results reveal that, contrary to the common belief, correlations between Rouge and the Pyramid scores are weak, which challenges its effectiveness for scientific summarization. Furthermore, we show a large variance of correlations between different Rouge variants and the manual evaluations which further makes the reliability of Rouge for evaluating scientific summaries less clear. We then propose an evaluation metric based on relevance analysis of summaries which aims to overcome the limitation of high lexical dependence in Rouge. We call our metric Sera (Summarization Evaluation by Relevance Analysis). Results show that the proposed metric achieves higher and more consistent correlations with semi-manual assessment scores.
[1]http://www.nist.gov/tac/2014/BiomedSumm/
Our contributions are as follows:
[2]The annotations can be accessed via the following repository: https://github.com/acohan/TAC-pyramid-Annotations/
ROUGE has been used as one of the main evaluation metrics in later summarization benchmarks such as TAC1 (Owczarzak and Dang, 2011). Since the establishment of ROUGE, almost all research in text summarization have used this metric as the main means for evaluating the quality of the proposed approaches. The public availability of ROUGE as a toolkit for summarization evaluation has contributed to its wide usage. While ROUGE has originally shown good correlations with human assessments, the study of its effectiveness was only limited to a few benchmarks on news summarization data (DUC2 2001-2003 benchmarks). Since 2003, summarization has grown to much further domains and genres such as scientific documents, social media and question answering.
|
In what areas has ROUGE been used in text summary evaluation?
|
Such as news, scientific documents, social media, and question answering.
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In the United States, what type of degree is typically earned before starting graduate school?
|
A Bachelors degree is earned before commencing grad school
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| 183
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Even though machine translation has improved considerably with the advent of neural machine translation (NMT) BIBREF0 , BIBREF1 , the translation of pronouns remains a major issue. They are notoriously hard to translate since they often require context outside the current sentence.
As an example, consider the sentences in Figure FIGREF1 . In both languages, there is a pronoun in the second sentence that refers to the European Central Bank. When the second sentence is translated from English to German, the translation of the pronoun it is ambiguous. This ambiguity can only be resolved with context awareness: if a translation system has access to the previous English sentence, the previous German translation, or both, it can determine the antecedent the pronoun refers to. In this German sentence, the antecedent Europäische Zentralbank dictates the feminine gender of the pronoun sie.
It is unfortunate, then, that current NMT systems generally operate on the sentence level BIBREF2 , BIBREF3 , BIBREF4 . Documents are translated sentence-by-sentence for practical reasons, such as line-based processing in a pipeline and reduced computational complexity. Furthermore, improvements of larger-context models over baselines in terms of document-level metrics such as BLEU or RIBES have been moderate, so that their computational overhead does not seem justified, and so that it is hard to develop more effective context-aware architectures and empirically validate them.
To address this issue, we present an alternative way of evaluating larger-context models on a test set that allows to specifically measure a model's capability to correctly translate pronouns. The test suite consists of pairs of source and target sentences, in combination with contrastive translation variants (for evaluation by model scoring) and additional linguistic and contextual information (for further analysis). The resource is freely available. Additionally, we evaluate several context-aware models that have recently been proposed in the literature on this test set, and extend existing models with parameter tying.
The main contributions of our paper are:
Section SECREF2 explains how our paper relates to existing work on context-aware models and the evaluation of pronoun translation. Section SECREF3 describes our test suite. The context-aware models we use in our experiments are detailed in Section SECREF4 . We discuss our experiments in Section SECREF5 and the results in Section SECREF6 .
Additionally, we evaluate several context-aware models that have recently been proposed in the literature on this test set, and extend existing models with parameter tying.
|
What models do the authors evaluate?
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They are context-aware models that have recently been proposed in the literature on this test set.
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Who is considered the bachelor president of the United States?
|
James Buchanan, the 15th president of the United States.
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| 65
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Word embeddings are representations of words in numerical form, as vectors of typically several hundred dimensions. The vectors are used as an input to machine learning models; for complex language processing tasks these are typically deep neural networks. The embedding vectors are obtained from specialized learning tasks, based on neural networks, e.g., word2vec BIBREF0, GloVe BIBREF1, FastText BIBREF2, ELMo BIBREF3, and BERT BIBREF4. For training, the embeddings algorithms use large monolingual corpora that encode important information about word meaning as distances between vectors. In order to enable downstream machine learning on text understanding tasks, the embeddings shall preserve semantic relations between words, and this is true even across languages.
Probably the best known word embeddings are produced by the word2vec method BIBREF5. The problem with word2vec embeddings is their failure to express polysemous words. During training of an embedding, all senses of a given word (e.g., paper as a material, as a newspaper, as a scientific work, and as an exam) contribute relevant information in proportion to their frequency in the training corpus. This causes the final vector to be placed somewhere in the weighted middle of all words' meanings. Consequently, rare meanings of words are poorly expressed with word2vec and the resulting vectors do not offer good semantic representations. For example, none of the 50 closest vectors of the word paper is related to science.
The idea of contextual embeddings is to generate a different vector for each context a word appears in and the context is typically defined sentence-wise. To a large extent, this solves the problems with word polysemy, i.e. the context of a sentence is typically enough to disambiguate different meanings of a word for humans and so it is for the learning algorithms. In this work, we describe high-quality models for contextual embeddings, called ELMo BIBREF3, precomputed for seven morphologically rich, less-resourced languages: Slovenian, Croatian, Finnish, Estonian, Latvian, Lithuanian, and Swedish. ELMo is one of the most successful approaches to contextual word embeddings. At time of its creation, ELMo has been shown to outperform previous word embeddings BIBREF3 like word2vec and GloVe on many NLP tasks, e.g., question answering, named entity extraction, sentiment analysis, textual entailment, semantic role labeling, and coreference resolution.
This report is split into further five sections. In section SECREF2, we describe the contextual embeddings ELMo. In Section SECREF3, we describe the datasets used and in Section SECREF4 we describe preprocessing and training of the embeddings. We describe the methodology for evaluation of created vectors and results in Section SECREF5. We present conclusion in Section SECREF6 where we also outline plans for further work.
At time of its creation, ELMo has been shown to outperform previous word embeddings (Peters et al., 2018) like word2vec and GloVe on many NLP tasks, e.g., question answering, named entity extraction, sentiment analy_x005f_x001f_sis, textual entailment, semantic role labeling, and corefer ence resolution.
|
In what aspects does the new model behave better?
|
It is better on many NLP tasks, e.g., question answering, named entity extraction, sentiment analy_x005f_x001f_sis, textual entailment, semantic role labeling, and corefer ence resolution.
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Classify the cities based on whether they are located in Eritrea or Poland.
Warsaw, Łódź, Assab, Keren, Kraków, Asmara
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Eritrea - Asmara, Keren, Assab
Poland - Warsaw, Kraków, Łódź
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1806.02847
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As previous systems collect relevant data from knowledge bases after observing questions during evaluation BIBREF24 , BIBREF25 , we also explore using this option. Namely, we build a customized text corpus based on questions in commonsense reasoning tasks. It is important to note that this does not include the answers and therefore does not provide supervision to our resolvers. In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions. The score for each document is a weighted sum of $F_1(n)$ scores when counting overlapping n-grams: $Similarity\_Score_{document} = \frac{\sum _{n=1}^4nF_1(n)}{\sum _{n=1}^4n}$
The top 0.1% of highest ranked documents is chosen as our new training corpus. Details of the ranking is shown in Figure 2 . This procedure resulted in nearly 1,000,000 documents, with the highest ranking document having a score of $8\times 10^{-2}$ , still relatively small to a perfect score of $1.0$ . We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events.
Figure 5 -left and middle show that STORIES always yield the highest accuracy for both types of input processing. We next rank the text corpora based on ensemble performance for more reliable results. Namely, we compare the previous ensemble of 10 models against the same set of models trained on each single text corpus. This time, the original ensemble trained on a diverse set of text corpora outperforms all other single-corpus ensembles including STORIES. This highlights the important role of diversity in training data for commonsense reasoning accuracy of the final system.
In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions.
We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events.
Figure 5 -left and middle show that STORIES always yield the highest accuracy for both types of input processing.
|
Which of their training domains improves performance the most?
|
The answers are shown as follows:
* documents from the CommonCrawl dataset that has the most overlapping n-grams with the question
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Why are dogs called dogs?
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About seven centuries ago, the word hound, which came from the Old English hund, was the word for all domestic canines. Dog was just used to refer to a subgroup of hounds that includes the lovely but frequently slobbering mastiff.
Of course, the opposite is now true. We use dog to talk about all of man’s best friends, from lovable golden retrievers to panting chow chows. And hound is now used to indicate a type of dog used just for hunting. Hound especially refers to a dog with a long face and large, droopy ears. Linguists still speculate about the reversal of fortune for hound and dog. One idea suggests that the sub-breed known as dogs became so populous that dog simply became the generic term (sort of an animal equivalent of the way brand names can become so ubiquitous that they start to be used as a general term for their purpose).
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Give me a summary about St Paul's Cathedral
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St Paul's Cathedral is an Anglican cathedral in London and is the seat of the Bishop of London. The cathedral serves as the mother church of the Diocese of London. It is on Ludgate Hill at the highest point of the City of London and is a Grade I listed building. Its dedication to Paul the Apostle dates back to the original church on this site, founded in AD 604. The present structure, dating from the late 17th century, was designed in the English Baroque style by Sir Christopher Wren. Its construction, completed in Wren's lifetime, was part of a major rebuilding programme in the city after the Great Fire of London. The earlier Gothic cathedral (Old St Paul's Cathedral), largely destroyed in the Great Fire, was a central focus for medieval and early modern London, including Paul's walk and St Paul's Churchyard, being the site of St Paul's Cross.
The cathedral is one of the most famous and recognisable sights of London. Its dome, surrounded by the spires of Wren's City churches, has dominated the skyline for over 300 years. At 365 ft (111 m) high, it was the tallest building in London from 1710 to 1963. The dome is still one of the highest in the world. St Paul's is the second-largest church building in area in the United Kingdom, after Liverpool Cathedral.
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What are 5 totally distinct, creative ways to use a glass of water?
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1. Pour half the water out and set the glass on a bedside table as a gentle reminder to look at life as "half full"
2. Dampen t-shirt so others perceive the water as sweat and assume you are working out super hard
3. Break the glass and use the shards to cut fabric similar to a knife or pair of scissors
4. Balance on top of head in order to improve focus and balance while risking getting wet
5. Quickly invert on a table and attempt to prevent water from escaping as a real-time test of liquid physics
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How are tornadoes formed?
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Tornadoes are rotating columns of air that is in contact with the ground. The catalyst that lead to tornadoes are temperature and humidity instability. Warm moist air near the ground, with cooler dry air at higher altitudes combined with wind shear, a change in wind speed and/or direction with height. This unstable airmass promotes the development of strong updrafts, while wind shear will further increase the strength of the updraft, and promotes the rotation from which tornadoes are produced. Tornado intensities are measured using the Enhanced Fujita (EF) scale
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How did you prepare for your business case competition?
|
To start off, we started getting background information on the topic before proceeding to construct our argument. Next, we finished a rough draft and checked the California FBLA website to make sure we were following the formatting requirements. By February break, we were completely finished with our prejudged report. After figuring out we were prejudged, we created a separate document for our speech. This was because our speech had additional requirements compared to the report; the speech also had to include our personal recommendation on the topic.
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How can I help to improve my son's tennis skills?
|
There are several options, depends on your son's current age, level.
If he is below 10, and he just starts, the most important thing for him is keeping interests in tennis. So you can find some local tennis group classes for him, or even semi-private lessons with qualified coaches.
If he is over 10, and he has some basic level skills already, he could attend some tournament (USTA junior circuit, L7 or even L6), or he could join one USTA jtt team, to play for the team with his friends. Also at this age and level, it is also good to have one private coach for him.
If he has already shown some competitive skills in this sport, you could also find some "big" tournaments, for example, Little Mo Tournament which is national wide tournament for young kids. You can even bring him to watch some live professional player's match.
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Mongolia (/mɒŋˈɡoʊliə/ (listen)) is a landlocked country in East Asia, bordered by Russia to the north and China to the south. It covers an area of 1,564,116 square kilometres (603,909 square miles), with a population of just 3.3 million, making it the world's most sparsely populated sovereign nation. Mongolia is the world's largest landlocked country that does not border a closed sea, and much of its area is covered by grassy steppe, with mountains to the north and west and the Gobi Desert to the south. Ulaanbaatar, the capital and largest city, is home to roughly half of the country's population.
|
Which major countries border Mongolia?
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Russia and China
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What is the best smartphone on the market?
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The answer to this question depends on multiple parameters, such as: 1.) do you prefer an iOS or an Android phone 2.) do you prefer a small display or a large display 3.) which benchmark parameters are relevant for you. AnTuTu is a well known benchmarking tool for mobile devices. As of their benchmarking in February 2023, the best performing iOS smartphone on the market is the iPhone 14 Pro, the best performing Android smartphone is the Red Magic 8 Pro+. Here is a link to the benchmark: https://www.antutu.com/en/ranking/rank1.htm
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Pick the best starting five from all players in NBA history
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Comparing players from different time periods is challenging due to the vastly different styles of play and differences in the rules. However, no NBA all time team would be complete without Michael Jordan. The rest of my lineup would include Stephen Curry, Lebron James, Tim Duncan, and Kareem Abdul-Jabbar.
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| 98
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Maximum matching (MM) is one of the most popular fundamental and structural segmentation algorithms for word segmentation BIBREF19 . This method is also considered as the Longest Matching (LM) in several research BIBREF9 , BIBREF3 . It is used for identifying word boundary in languages like Chinese, Vietnamese and Thai. This method is a greedy algorithm, which simply chooses longest words based on the dictionary. Segmentation may start from either end of the line without any difference in segmentation results. If the dictionary is sufficient BIBREF19 , the expected segmentation accuracy is over 90%, so it is a major advantage of maximum matching . However, it does not solve the problem of ambiguous words and unknown words that do not exist in the dictionary.
There are two types of the maximum matching approach: forward MM (FMM) and backward MM (BMM). FMM starts from the beginning token of the sentence while BMM starts from the end. If the sentence has word boundary ambiguities, the output of FMM and BMM will be different. When applying FMM and BMM, there are two types of common errors due to two ambiguities: overlapping ambiguities and combination ambiguity. Overlapping ambiguities occur when the text AB has both word A, B and AB, which are in the dictionary while the text ABC has word AB and BC, which are in the dictionary. For example, "cụ già đi nhanh quá" (there two meanings: ”the old man goes very fast” or ”the old man died suddenly”) is a case of the overlapping ambiguity while "tốc độ truyền thông tin" is a case of the combination ambiguity.
As shown in Figure 2, the method simplification ambiguities, maximum matching is the first step to get features for the modelling stage in machine learning systems, like Conditional Random Fields or Support Vector Machines.
Maximum matching (MM) is one of the most popular fundamental and structural segmentation algorithms for word segmentation BIBREF19 . This method is also considered as the Longest Matching (LM) in several research BIBREF9 , BIBREF3 . It is used for identifying word boundary in languages like Chinese, Vietnamese and Thai. This method is a greedy algorithm, which simply chooses longest words based on the dictionary. Segmentation may start from either end of the line without any difference in segmentation results. If the dictionary is sufficient BIBREF19 , the expected segmentation accuracy is over 90%, so it is a major advantage of maximum matching . However, it does not solve the problem of ambiguous words and unknown words that do not exist in the dictionary.
There are two types of the maximum matching approach: forward MM (FMM) and backward MM (BMM). FMM starts from the beginning token of the sentence while BMM starts from the end. If the sentence has word boundary ambiguities, the output of FMM and BMM will be different. When applying FMM and BMM, there are two types of common errors due to two ambiguities: overlapping ambiguities and combination ambiguity. Overlapping ambiguities occur when the text AB has both word A, B and AB, which are in the dictionary while the text ABC has word AB and BC, which are in the dictionary. For example, "cụ già đi nhanh quá" (there two meanings: ”the old man goes very fast” or ”the old man died suddenly”) is a case of the overlapping ambiguity while "tốc độ truyền thông tin" is a case of the combination ambiguity.
As shown in Figure 2, the method simplification ambiguities, maximum matching is the first step to get features for the modelling stage in machine learning systems, like Conditional Random Fields or Support Vector Machines.
Maximum matching (MM) is one of the most popular fundamental and structural segmentation algorithms for word segmentation [20]. ****However, it does not solve the problem of ambiguous words and unknown words that do not exist in the dictionary.
|
What is the limitation of Maximum Matching?
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It does not solve the problem of ambiguous words and unknown words that do not exist in the dictionary.
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which countries has the most diverse food?
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Countries with vast areas with various geological structures, profound histories, or chill life styles usually have a wide diversity of food choices. For example, China has 3.7 million square miles area and over 50 different ethnitical groups. Each ethnicity has its own culture and living styles, thus different cuisine styles and tastes. The United States is another example with almost 3.8 million square miles of area and immigrants from all over the world. As a results, the immigrants and their descendants bring in food culture from their original heritage that creates a huge variety of choices in the US. South European areas like Rome have stellar reputation for their food too. When it comes to pizza, pasta, seafood, coffee and gelato, no one could say no when visiting Italy.
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1911.00547
| false
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2. We proposed joint learning NLP models that use convolutional neural network (CNN) BIBREF8 and bi-directional long short-term memory (BiLSTM) BIBREF9, BIBREF10 as basic units. Our models can automatically extract the key elements from the sexual harassment stories and at the same time categorize the stories in different dimensions. The proposed models outperformed the single task models, and achieved higher than previously reported accuracy in classifications of harassment forms BIBREF6.
We proposed joint learning NLP models that use convolutional neural network (CNN) BIBREF8 and bi-directional long short-term memory (BiLSTM) BIBREF9, BIBREF10 as basic units. Our models can automatically extract the key elements from the sexual harassment stories and at the same time categorize the stories in different dimensions. The proposed models outperformed the single task models, and achieved higher than previously reported accuracy in classifications of harassment forms BIBREF6.
|
What model did they use?
|
The answers are shown as follows:
* joint learning NLP models that use convolutional neural network (CNN) BIBREF8 and bi-directional long short-term memory (BiLSTM)
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Cynodon dactylon, commonly known as Bermuda grass, is a grass found worldwide. It is native to Europe, Africa, Australia and much of Asia. It has been introduced to the Americas. Contrary to its common name, it is not native to Bermuda and is in fact an abundant invasive species there. In Bermuda it has been known as "crab grass" (also a name for Digitaria sanguinalis). Other names are Dhoob, dūrvā grass, ethana grass, dubo, dog grass, dog's tooth grass, Bahama grass, crab grass, devil's grass, couch grass, Indian doab, arugampul, grama, wiregrass and scutch grass.
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Where did Bermuda grass come from?
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Cynodon dactylon, commonly known as Bermuda grass, is a grass found worldwide. It is native to Europe, Africa, Australia and much of Asia.
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Wilson's first TV appearance was for GMA Network 7's Kakabakaba Adventure (2003) where she was a regular cast member until the show ended in 2004. Between 2003 and 2011, Wilson appeared and starred in several TV series and soap operas. With supporting roles in All Together Now (2004), Encantadia (2005), Darna (2005), Asian Treasures (2007) and Joaquin Bordado (2008). As part of the main cast in Saang Sulok ng Langit (2005), where she was nominated as Best TV Villain, My Guardian Abby (2006), Darna (2009) and Beauty Queen (2010).
|
When did Maggie Wilson first appear on TV?
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Wilson first appeared on TV on Kakabakaba Adventure in 2003.
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What are 5 unique recipes for any kind of potatoes?
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Here is a list of 5 unique recipes with different kinds of potatoes:
Taro Boba Tea
Ingredients
150 g taro, peeled and cubed
2 teaspoon sugar salt
3 tablespoon boba
2 teaspoon sugar
1/2 cup ceylon tea, strongly brewed (can use any other black tea)
200 ml milk (can use plant based milk)
1 teaspoon condensed milk
Instructions
Boil the cubed taro root for 20 minutes on medium heat. It should be soft enough to poke through with a fork. Discard the water. Smash the cooked ground root until an even paste is formed. For a smoother paste, use a food processor or blender. Incorporate the sugar while the paste is still hot. Set aside. Add your boba to a pot of boiling water. Add 2 teaspoon of sugar and allow this boil for 5-7 minutes. Once the boba begin to float these are done. Adjust the timing depending on how firm or soft you prefer them. Remove from hot water and set them aside. Brew the ceylon and let this mix well with fresh ground root paste and condensed milk. Finally, add the milk and tapioca pearls to the mixture. Serve the taro milk tea hot or cold.
Sweet Potato Pie
Ingredients
3 medium sweet potatoes, baked
1/2 cup butter
1 cup granulated sugar
2 teaspoons vanilla extract
2 large eggs beaten
1/2 cup milk, whole or evaporated
1 (9-inch) unbaked pie crust
Optional Toppings
whipped cream
marshmallow fluff
Instructions
Preheat the oven to 400º F. Scrub the sweet potatoes until clean, prick them 4 to 5 times with a fork. Place onto a baking sheet and bake for 45 - 50 minutes until the sweet potatoes are tender when pricked with a toothpick. Remove from the oven and allow to cool until they can easily be handled. Peel the skin from the sweet potatoes and place the sweet potatoes into a large mixing bowl. Reduce the oven heat to 350º F.
Add butter to the sweet potatoes and mash until smooth. Add the sugar(s) to the sweet potatoes and mix until well combined. Add the vanilla extract, milk, and the eggs. Mix until well combined. Pour into the unbaked pie crust.
Bake the pie until the center of the pie is set, about 1 hour. Remove the pie from the oven and allow to cool slightly.
Serve the pie warm or allow to cool before slicing. Top with whipped cream (or your favorite topping) and serve.
Gnocchi
Ingredients
2 potatoes, peeled
2 cups all-purpose flour
1 egg
Instructions
Bring a large pot of salted water to a boil; add potatoes and cook until tender but still firm, about 15 minutes. Drain, cool, and mash with a fork or potato masher.
Combine 1 cup mashed potato, flour, and egg in a large bowl. Knead until dough forms a ball. Shape small portions of the dough into long "snakes". On a floured surface, cut snakes into 1/2-inch pieces.
Bring a large pot of lightly salted water to a boil. Drop in gnocchi and cook for 3 to 5 minutes or until gnocchi have risen to the top; drain and serve.
Potato Latkes
Ingredients
2 1/2 pounds baking potatoes, coarsely shredded
1 medium onion, coarsely shredded
2 large eggs, beaten
2 teaspoons kosher salt
1/2 teaspoon black pepper
Vegetable oil, for frying
Applesauce, sour cream, smoked salmon and salmon roe, for serving
Instructions
In a colander set over a large bowl, toss the potatoes with the onion and squeeze dry. Let the potatoes and onion drain for 2 to 3 minutes, then pour off the liquid in the bowl, leaving the starchy paste at the bottom. Add the potatoes and onion, along with the eggs, salt, pepper and mix well.
In a large skillet, heat an 1/8-inch layer of oil until shimmering. Spoon 1/4-cup mounds of the latke batter into the skillet about 2 inches apart and flatten slightly with a spatula. Fry the latkes over moderately high heat, turning once, until golden and crisp, 5 to 7 minutes. Transfer the latkes to paper towels to drain, then transfer to a platter. Repeat to make the remaining latkes, adding more oil to the skillet as needed.
Serve with applesauce, sour cream, smoked salmon and salmon roe.
Poutine
Ingredients
3 or 4 large russet potatoes ( 3 to 4 pounds total), peeled and cut into 1/3-inch-wide sticks
2 tablespoons extra-virgin olive oil
1 teaspoon sea salt
1 1/2 teaspoons dried thyme
Freshly cracked black pepper
4 tablespoons (½ stick) unsalted butter
1/4 cup all-purpose flour
3/4 teaspoon garlic powder
½ teaspoon onion powder
1 cup chicken broth
1 cup beef broth
1 ½ teaspoons Worcestershire sauce
1 teaspoon Better Than Bouillon beef base or 1 beef bouillon cube
1 teaspoon unfiltered apple cider vinegar
10 ounces white cheddar cheese curds, at room temperature
Minced fresh parsley
Instructions
Make the fries. Place the batons in a large bowl and cover them with cold water. Let soak for at least 30 minutes up to 24 hours for crispier fries.
Preheat the oven to 425°F. Line 2 large rimmed baking sheets with parchment paper.
Spread the potatoes out onto large, clean kitchen towels. Dry them off as best as possible. Place the potatoes on the prepared baking sheets, drizzle with the olive oil, sprinkle with the thyme and salt and add black pepper to taste. Toss to coat evenly, then spread out the potatoes into a single layer.
Bake for 10 minutes, then toss the fries and rotate the pans, swapping the one on the top for the one on the bottom. Increase the oven to 450°F and bake the fries for 10 minutes more.Toss the fries and swap the pans from top to bottom again. Turn on the broiler and cook the fries until they are beginning to brown and crisp, about 5 more minutes. Continue to toss and broil until the fries reach your desired doneness. Season with salt and pepper.
Meanwhile, make the gravy. In a large saucepan set over medium heat, melt the butter. Sprinkle the flour over the butter and cook, stirring constantly, until golden brown, about 2 minutes. Stir in the garlic powder and onion powder. Slowly add the chicken and beef broth. Increase the heat to medium high and bring to a boil, stirring often. Reduce the heat to low and simmer until thickened. Stir in the Worcestershire, beef bouillon, and vinegar. Season with salt and pepper.
To assemble the poutine, divide the fries among 4 plates. Sprinkle it with the cheese curds, drizzle on the gravy, and garnish with parsley. Serve immediately.
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Tell me whether the following athletes are tennis or football players: Ibrahimovic, Medvedev, Agassi, Neymar, Halep, Davenport
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There are only two football players in the list, Neymar and Ibrahimovic. All the others are well known tennis players.
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Identify which animal species is alive or extinct: Megatherium, Sri Lankan Elephant
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Megatherium is extinct, Sri Lankan Elephant is alive.
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What are the words of House Velaryon?
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"The Old, the True, the Brave"
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Although committed to preserving the Soviet state and its Marxist–Leninist ideals, Gorbachev believed significant reform necessary for survival. He withdrew troops from the Soviet–Afghan War and embarked on summits with United States president Ronald Reagan to limit nuclear weapons and end the Cold War. Domestically, his policy of glasnost ("openness") allowed for enhanced freedom of speech and press, while his perestroika ("restructuring") sought to decentralize economic decision-making to improve its efficiency. His democratization measures and formation of the elected Congress of People's Deputies undermined the one-party state. Gorbachev declined to intervene militarily when various Eastern Bloc countries abandoned Marxist–Leninist governance in 1989–1992. Internally, growing nationalist sentiment threatened to break up the Soviet Union, leading Marxist–Leninist hardliners to launch the unsuccessful August Coup against Gorbachev in 1991. In the coup's wake, the Soviet Union dissolved against Gorbachev's wishes. After resigning from the presidency, he launched the Gorbachev Foundation, became a vocal critic of Russian presidents Boris Yeltsin and Vladimir Putin, and campaigned for Russia's social-democratic movement.
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Given this reference text about Gorbachev, tell me whether he was an ally of Yeltsin and Putin.
|
According to this passage, Gorbachev was neither an ally of Yeltsin nor of Putin. In fact, he was a vocal critic of both of them.
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Which characters belong to DC or Marvel Universe? Ray Palmer, Ant-Man
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Ant-Man is Marvel, Ray Palmer is DC
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The Pleasance is a street just outside the Old Town of Edinburgh, Scotland, a remnant of the Flodden Wall flanking the west side of the street between Drummond Street and the Cowgate. Historically, the street was one of the main routes into Edinburgh from the south, meeting St Mary's Wynd (now St Mary's Street) at St Mary's Wynd Port, one of the gateways of the town walls. The name derives from the Scots plesance, meaning a park or garden. It first appears in 1507 as the name of a nearby house, and was later transferred to the street and then the suburb which was part of the regality of the Canongate. The derivation of the name from a nunnery of St Mary of Placentia, often mentioned in histories of Edinburgh, is an invention by William Maitland in his 1753 History of Edinburgh.
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What is the Scots word that The Pleasance is derived from?
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plesance
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Are you going for a walk today?
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I'll go for a walk today, the weather is finally perfect! I've been staying indoors lately due to rain, but with this sun shining, I can't stay indoors any longer!
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| 253
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As an essential part of a task-oriented dialogue system BIBREF0 , the task of natural language generation (NLG) is to produce a natural language utterance containing the desired information given a semantic representation consisting of dialogue act types with a set of slot-value pairs. Conventional methods using hand-crafted rules often generates monotonic utterances and it requires substantial amount of human engineering work. Recently, various neural approaches BIBREF1 , BIBREF2 , BIBREF3 have been proposed to generate accurate, natural and diverse utterances. However, these methods are typically developed for particular domains. Moreover, they are often data-intensive to train. The high annotation cost prevents developers to build their own NLG component from scratch. Therefore, it is extremely useful to train a NLG model that can be generalized to other NLG domains or tasks with a reasonable amount of annotated data. This is referred to low-resource NLG task in this paper.
Recently, some methods have been proposed for low-resource NLG tasks. Apart from the simple data augmentation trick BIBREF4 , specialized model architectures, including conditional variational auto-encoders (CVAEs, BIBREF3 , BIBREF5 , BIBREF6 ) and adversarial domain adaptation critics BIBREF5 , have been proposed to learn domain-invariant representations. Although promising results were reported, we found that datasets used by these methods are simple which tend to enumerate many slots and values in an utterance without much linguistic variations. As a consequence, over-fitting the slots and values in the low-resource target domain could even outperform those versions trained with rich source domain examples BIBREF6 . Fortunately, there is a new large-scale dialog dataset (MultiWoz, BIBREF7 ) that contains a great variety of domains and linguistic patterns that allows us to conduct extensive and meaningful experimental analysis for low-resource NLG tasks.
In this paper, instead of casting the problem as model-based approaches, we propose a generalized optimization-based meta-learning approach to directly enhance the optimization procedure for the low-resource NLG task. We start by arguing that a recently proposed model-agnostic meta-learning algorithm (MAML, BIBREF8 ) is a nice fit to the low-resource NLG task. Then, we proposed a generalized NLG algorithm called Meta-NLG based on MAML by viewing languages in different domains or dialog act types as separate Meta NLG tasks. Following the essence of MAML, the goal of Meta-NLG is to learn a better initialization of model parameters that facilitates fast adaptation to new low-resource NLG scenarios. As Meta-NLG is model-agnostic as long as the model can be optimized by gradient descent, we could apply it to any existing NLG models to optimize them in a way that adapt better and faster to new low-resource tasks.
The main contribution of this paper is two-fold:
Although promising results were reported, we found that datasets used by these methods are simple which tend to enumerate many slots and values in an utterance without much linguistic variations.
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Are datasets used by these methods simple?
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Yes, they are.
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Give me a list of 10 books on building better habits
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1. "Atomic Habits" by James Clear
2. "The Power of Habit" by Charles Duhigg
3. "The 7 Habits of Highly Effective People" by Stephen R. Covey
4. "How to Change" by Katy Milkman
5. "Better than Before" by Gretchen Rubin
6. "Tiny Habits" by BJ Fogg
7. "Elastic Habits" by Stephen Guise
8. "Good Habits, Bad Habits" by Wendy Wood
9. "The Habit Blueprint" by Patrik Edblad
10. "The Now habit" by Neil Fiore
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The event was established in 1855, and it was originally called the Prix de l'Empereur. It was initially held at the Champ de Mars, and was transferred to Longchamp in 1857. It was cancelled due to the Franco-Prussian War in 1871, and was renamed the Grande Poule des Produits in 1872.
The race was one of several trials for the Prix du Jockey Club collectively known as the Poules des Produits. The others (listed by their modern titles) were the Prix Daru, the Prix Hocquart, the Prix Noailles and the Prix Greffulhe. Unlike those races, the Grande Poule des Produits had no restrictions based on the nationality of a horse's sire or dam.
The event was renamed in memory of Auguste Lupin (1807–1895), a successful owner-breeder, in 1896.
The Prix Lupin was abandoned throughout World War I, with no running from 1915 to 1918. It was cancelled once during World War II, in 1940. It was run at Le Tremblay over 2,150 metres in 1943, and at Maisons-Laffitte in 1944.
The present system of race grading was introduced in 1971, and the Prix Lupin was classed at Group 1 level.
The race was last run in 2004. It was discontinued after France Galop restructured its Group 1 programme for three-year-olds in 2005.
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Why did the Prix Lupin not take place from 1915 to 1918?
|
Due to the first World War
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What are some important things to bring on a bicycle ride.
|
There are many things that you may want to bring on a bicycle ride. A tire repair kit is important for bicycle rides in case you get a flat tire. You should also wear a helmet to protect against serious injury in case of an accident. Some other useful things to bring on a bike ride may include water, snacks, sun glasses, sun screen, and a local map.
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Did you know the Earth is flat?
|
No, that is incorrect. While people once believed the world was flat, there is scientific evidence that the Earth is actually an irregularly shaped ellipsoid
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Zeus (Ζεύς, Zeús)
King of the gods, ruler of Mount Olympus, and god of the sky, weather, thunder, lightning, law, order, and justice. He is the youngest son of Cronus and Rhea. He overthrew Cronus and gained the sovereignty of heaven for himself. In art he is depicted as a regal, mature man with a sturdy figure and dark beard. His usual attributes are the royal scepter and the lightning bolt. His sacred animals include the eagle and the bull. His Roman counterpart is Jupiter, also known as Jove.
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From the passage identify the names of Zeus’s Roman counterpart. Display the results in a comma separated format.
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Jupiter, Jove
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Slow Burn is a 1989 film directed by John Eyres and starring Ivan Rogers, William Smith, Anthony James and Stephen M.D. Chang. A veteran cop's partner is killed by a Mafia hitman, and he goes after him. Also on the trail of the hitman is a rookie cop who has his own personal reasons for pursuing him.
|
There is a movie "Slow burn". Can you help me summarize some important points about this movie
|
1. The 1989 movie Slow Burn, starring Ivan Rogers, William Smith, Anthony James, and Stephen M.D. Chang, was directed by John Eyres.
2. A Mafia hitman kills a veteran cop's partner, and the veteran cop then pursues the hitman.
3. A rookie cop who is pursuing the hitman for personal reasons is also after him.
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1901.03438
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The effectiveness and ubiquity of pretrained sentence embeddings for natural language understanding has grown dramatically in recent years. Recent sentence encoders like OpenAI's Generative Pretrained Transformer BIBREF3 and BERT BIBREF2 achieve the state of the art on the GLUE benchmark BIBREF4 . Among the GLUE tasks, these state-of-the-art systems make their greatest gains on the acceptability task with the Corpus of Linguistic Acceptability BIBREF0 . CoLA contains example sentences from linguistics publications labeled by experts for grammatical acceptability, and written to show subtle grammatical features. Because minimal syntactic differences can separate acceptable sentences from unacceptable ones (What did Bo write a book about? / *What was a book about written by Bo?), and acceptability classifiers are more reliable when trained on GPT and BERT than on recurrent models, it stands to reason that GPT and BERT have better implicit knowledge of syntactic features relevant to acceptability.
Among the GLUE tasks, these state-of-the-art systems make their greatest gains on the acceptability task with the Corpus of Linguistic Acceptability BIBREF0 . CoLA contains example sentences from linguistics publications labeled by experts for grammatical acceptability, and written to show subtle grammatical features. Because minimal syntactic differences can separate acceptable sentences from unacceptable ones (What did Bo write a book about? / *What was a book about written by Bo?), and acceptability classifiers are more reliable when trained on GPT and BERT than on recurrent models, it stands to reason that GPT and BERT have better implicit knowledge of syntactic features relevant to acceptability.
|
Where does the data in CoLA come from?
|
The answers are shown as follows:
* CoLA contains example sentences from linguistics publications labeled by experts
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1812.10860
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Looking to other target tasks, the grammar-related CoLA task benefits dramatically from ELMo pretraining: The best result without language model pretraining is less than half the result achieved with such pretraining. In contrast, the meaning-oriented textual similarity benchmark STS sees good results with several kinds of pretraining, but does not benefit substantially from the use of ELMo.
Looking to other target tasks, the grammar-related CoLA task benefits dramatically from ELMo pretraining: The best result without language model pretraining is less than half the result achieved with such pretraining. In contrast, the meaning-oriented textual similarity benchmark STS sees good results with several kinds of pretraining, but does not benefit substantially from the use of ELMo.
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Do some pretraining objectives perform better than others for sentence level understanding tasks?
|
Yes.
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| 183
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We automatically create a test set from the OpenSubtitles corpus BIBREF22 . The goal is to provide a large number of difficult test cases where an English pronoun has to be translated to a German pronoun.
The most challenging cases are translating it to either er, sie or es, depending on the grammatical gender of the antecedent. Not only is the translation of it ambiguous, there is also class imbalance in the training data (see Table TABREF18 ). There is roughly a 30% probability that it is aligned to es, which makes it difficult to learn to translate er and sie. We use parsing and automatic co-reference resolution to find translation pairs that satisfy our constraints.
To provide a basis for filtering with constraints, we tokenize the whole data set with the Moses tokenizer, generate symmetric word alignments with fast_align BIBREF23 , parse the English text with CoreNLP BIBREF24 , parse the German text with ParZu BIBREF25 and perform coreference resolution on both sides. The coreference chains are obtained with the neural model of CoreNLP for English, and with CorZu for German BIBREF26 , respectively.
Then we opt for high-precision, aggressive filtering, according to the following protocol: for each pair of sentences INLINEFORM0 in English and German, extract iff
INLINEFORM0 contains the English pronoun it, and INLINEFORM1 contains a German pronoun that is third person singular (er, sie or es), as indicated by their part-of-speech tags;
those pronouns are aligned to each other;
both pronouns are in a coreference chain;
their nominal antecedents in the coreference chain are aligned on word level.
This removes most candidate pairs, but is necessary to overcome the noise introduced by our preprocessing pipeline, most notably coreference resolution. From the filtered set, we create a balanced test set by randomly sampling 4000 instances of each of the three translations of it under consideration (er, sie, es). We do not balance antecedent distance. See Table TABREF25 for the distribution of pronoun pairs and antecedent distance in the test set.
For each sentence pair in the resulting test set, we introduce contrastive translations. A contrastive translation is a translation variant where the correct pronoun is swapped with an incorrect one. For an example, see Table TABREF19 , where the pronoun it in the original translation corresponds to sie because the antecedent bat is a feminine noun in German (Fledermaus). We produce wrong translations by replacing sie with one of the other pronouns (er, es).
Note that, by themselves, these contrastive translations are grammatically correct if the antecedent is outside the current sentence. The test set also contains pronouns with an antecedent in the same sentence (antecedent distance 0). Those examples do not require any additional context for disambiguation and we therefore expect the sentence-level baseline to perform well on them.
We take extra care to ensure that the resulting contrastive translations are grammatically correct, because ungrammatical sentences are easily dismissed by an NMT system. For instance, if there are any possessive pronouns (such as seine) in the sentence, we also change their gender to match the personal pronoun replacement.
The German coreference resolution system does not resolve es because most instances of es in German are either non-referential forms, or they refer to a clause instead of a nominal antecedent. We limit the test set to nominal antecedents, as these are the only ambiguous cases with respect to translation. For this reason, we have to rely entirely on the English coreference links for the extraction of sentence pairs with it INLINEFORM0 es, as opposed to pairs with it INLINEFORM1 er and it INLINEFORM2 sie where we have coreference chains in both languages.
Our extraction process respects document boundaries, to ensure we always search for the right context. We extract additional information from the annotated documents, such as the distance (in sentences) between pronouns and their antecedents, the document of origin, lemma, morphology and dependency information if available.
To provide a basis for filtering with constraints, we tokenize the whole data set with the Moses tokenizer, generate symmetric word alignments with fast_align (Dyer et al., 2013), parse the English text with CoreNLP (Manning et al., 2014), parse the German text with ParZu (Sennrich et al., 2013) and perform coreference resolution on both sides.
|
What is used to provide a basis for filtering with constraints?
|
To provide a basis for filtering with constraints, the authors tokenize the whole data set with the Moses tokenizer, generate symmetric word alignments with fast_align, parse the English text with CoreNLP , parse the German text with ParZu and perform coreference resolution on both sides.
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| 171
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Crowdsourced user studies are widely used for related tasks, such as evaluating dialogue strategies, e.g. BIBREF26, and for eliciting a moral stance from a population BIBREF27. Our crowdsourced setup is similar to an “overhearer experiment” as e.g. conducted by Ma:2019:handlingChall where study participants were asked to rate the system's emotional competence after watching videos of challenging user behaviour. However, we believe that the ultimate measure for abuse mitigation should come from users interacting with the system. chin2019should make a first step into this direction by investigating different response styles (Avoidance, Empathy, Counterattacking) to verbal abuse, and recording the user's emotional reaction – hoping that eliciting certain emotions, such as guilt, will eventually stop the abuse. While we agree that stopping the abuse should be the ultimate goal, BIBREF28's study is limited in that participants were not genuine (ab)users, but instructed to abuse the system in a certain way. BIBREF29 report that a pilot using a similar setup let to unnatural interactions, which limits the conclusions we can draw about the effectiveness of abuse mitigation strategies. Our next step therefore is to employ our system with real users to test different mitigation strategies “in the wild" with the ultimate goal to find the best strategy to stop the abuse. The results of this current paper suggest that the strategy should be adaptive to user type/ age, as well as to the severity of abuse.
Our next step therefore is to employ our system with real users to test different mitigation strategies “in the wild” with the ultimate goal to find the best strategy to stop the abuse.
|
What is the authors' ultimate goal?
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The ultimate goal is to find the best strategy to stop the abuse.
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Tell me whether these cities are in Minnesota, Arizona or neither.
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Duluth - Minnesota
Bisbee - Arizona
Bend - Neither
St Joseph - Minnesota
Why - Arizona
Guaymas - Neither
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Why do people like playing video games?
|
Video games offer full immersion into a complex world where decisions and actions affect the state of the world and outcomes. With video games, a player can be racing classic cars on the beaches of Europe and then immediately be playing a wizard during medieval times. Video games not only stimulate the mind improving decision making skills and cognitive reasoning, but also strengthens digit dexterity and coordination. Additionally, the activity of playing video games is fun! Especially when it comes to multiplayer games; it's always fun to play with your friends. Personally, I like the stories that video games tell in such meticulous detail.
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Where is texas?
|
Texas is one of the 50 states in the United States. It is located in the center and central part of the United States. It has a coastline along the Gulf of Mexico that extends 350 miles, and a coastline along the Mexico border than extends nearly 2000 miles. The population of Texas is about 30 million people.
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According to traditional color theory based on subtractive primary colors and the RYB color model, yellow mixed with purple, orange mixed with blue, or red mixed with green produces an equivalent gray and are the painter's complementary colors. These contrasts form the basis of Chevreul's law of color contrast: colors that appear together will be altered as if mixed with the complementary color of the other color. A piece of yellow fabric placed on a blue background will appear tinted orange because orange is the complementary color to blue.
However, when complementary colors are chosen based on the definition by light mixture, they are not the same as the artists' primary colors. This discrepancy becomes important when color theory is applied across media. Digital color management uses a hue circle defined according to additive primary colors (the RGB color model), as the colors in a computer monitor are additive mixtures of light, not subtractive mixtures of paints.
|
Based on the given text, can you please. list some important aspects of colour theory
|
1. The painter's complementary colour based on subtractive primary colour and the RYB colour model, can be made by mixing yellow with purple, orange with blue, or red with green and it produces an equivalent gray.
2. Since orange is complimentary colour of blue, A piece of yellow fabric placed on a blue background will appear as orange.
3. When complementary colours are chosen based on the definition of light mixture, they are not the same as the primary colour.
4. The digital colour management uses a hue circle of additive primary colour also known as the RGB colour model.
5. The colorus in a computer monitor are additive mixtures of light.
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There was a dispute between Crosby and the sponsor over what theme tune should be used. Woodbury Soap and its agency, Lennon & Mitchell, considered that the tune ‘Loveliness’ which had always been the theme of the Woodbury programs should continue. Crosby argued that the logical theme for the stanza was "Where the Blue of the Night (Meets the Gold of the Day)", on the grounds that the song had been identified with him ever since he had been on the air. Eventually Woodbury conceded to the singer. Other problems arose with Dale Winbrow, who had been sent out to the West coast from New York by the Lennon & Mitchell agency to resolve the theme tune argument and supervise the show. Winbrow listened to the program that Crosby and Lennie Hayton had prepared for the next broadcast and was very critical of it. “The flare up that was provoked from Crosby wound up with the warbler and Hayton declaring themselves out of the show. The baritone’s management later prevailed upon him to hold on while the situation was being straightened out with the agency on the New York end.”
|
Summarize the argument and tell me who won
|
Crosby and the sponsor, Woodbury Soap, argued over what theme tune should be used. Woodbury Soap and its agency, Lennon & Mitchell, argued that ‘Loveliness’ should be chosen because it had always been used for Woodbury programs. Crosby disagreed and insisted that "Where the Blue of the Night (Meets the Gold of the Day)" would be better given that the song had been identified with him ever since he had been on the air.
In the end, Crosby won the argument as Woodbury conceded to the singer.
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What are the five highest peaks in the world, their altitude, and the countries where are they located?
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- Everest Mount, 8849 meters, is located in the Himalayas chain at the Nepalese border with China
- K2, 8611 meters, located in the Karakoram mountain range in Pakistan and China
- Kangchenjunga, 8586 meters, located in the Himalaya chain across Nepal & India
- Lhotse, 8,516 meters, still located in the Himalayas chain in China and Nepal
- Makalu, 8,485 meters, located in the Himalayas chain across Nepal and China
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Which ones are programming languages, out of C, Java, Node.js, Python, HTML, BigData, Spark?
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Well, C, Java and Python are programming languages.
While HTML is a language, it is a markup language and not a programming language. Node.js is actually a framework built on top of Javascript, so it can't be classified as a language.
BigData is a broader term used for storing, processing and usage of large amounts of data. Spark is one of the most popular distributed data processing frameworks. Spark can be used from multiple languages.
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| 47
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There have been many implementations of the word2vec model in either of the two architectures it provides: continuous skipgram and CBoW (BIBREF0). Similar distributed models of word or subword embeddings (or vector representations) find usage in sota, deep neural networks like BERT and its successors (BIBREF1, BIBREF2, BIBREF3). These deep networks generate contextual representations of words after been trained for extended periods on large corpora, unsupervised, using the attention mechanisms (BIBREF4).
It has been observed that various hyper-parameter combinations have been used in different research involving word2vec with the possibility of many of them being sub-optimal (BIBREF5, BIBREF6, BIBREF7). Therefore, the authors seek to address the research question: what is the optimal combination of word2vec hyper-parameters for intrinsic and extrinsic NLP purposes? There are astronomically high numbers of combinations of hyper-parameters possible for neural networks, even with just a few layers. Hence, the scope of our extensive work over three corpora is on dimension size, training epochs, window size and vocabulary size for the training algorithms (hierarchical softmax and negative sampling) of both skipgram and CBoW. The corpora used for word embeddings are English Wiki News Abstract by BIBREF8 of about 15MB, English Wiki Simple (SW) Articles by BIBREF9 of about 711MB and the Billion Word (BW) of 3.9GB by BIBREF10. The corpus used for sentiment analysis is the IMDb dataset of movie reviews by BIBREF11 while that for NER is Groningen Meaning Bank (GMB) by BIBREF12, containing 47,959 sentence samples. The IMDb dataset used has a total of 25,000 sentences with half being positive sentiments and the other half being negative sentiments. The GMB dataset has 17 labels, with 9 main labels and 2 context tags. It is however unbalanced due to the high percentage of tokens with the label 'O'. This skew in the GMB dataset is typical with NER datasets.
The objective of this work is to determine the optimal combinations of word2vec hyper-parameters for intrinsic evaluation (semantic and syntactic analogies) and extrinsic evaluation tasks (BIBREF13, BIBREF14), like SA and NER. It is not our objective in this work to record sota results. Some of the main contributions of this research are the empirical establishment of optimal combinations of word2vec hyper-parameters for NLP tasks, discovering the behaviour of quality of vectors viz-a-viz increasing dimensions and the confirmation of embeddings being task-specific for the downstream. The rest of this paper is organised as follows: the literature review that briefly surveys distributed representation of words, particularly word2vec; the methodology employed in this research work; the results obtained and the conclusion.
The objective of this work is to determine the optimal combinations of word2vec hyper-parameters for intrinsic evaluation (semantic and syntactic analogies) and extrinsic evaluation tasks, like SA and NER.
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What tasks do they determine the optimal combinations of word2vec hyper-parameters for?
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Intrinsic evaluation and extrinsic evaluation tasks.
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List some characters from South Park.
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Eric Cartman, Kenny McCormick, Butters Stotch, Stan Marsh, Kyle Broflovski, Tweek Tweak, Mr. Garrison, Mr. Hat, Craig Tucker
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| 514
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It is possible that the observed robustness of SoftHebb can be reproduced by other unsupervised learning rules. To test this possibility, we compared SoftHebb with PCA and k-means. We used 100 neurons, principal components, or centroids respectively. In PCA and k-means we then treated the learned coefficients as weight vectors of neurons and applied an activation function to then train a supervised classifier on top. First we attempted softmax as in SoftHebb. However the unperturbed test accuracy achieved at convergence was much lower. For example, on MNIST, k-means only reached an accuracy of 53.64% and PCA 28.55%, whereas SoftHebb reached 91.06. Therefore, we performed the experiment again, but with ReLU activation for k-means and PCA, reaching 90.61% and 82.74% respectively. Then we tested for robustness, revealing that SoftHebb’s learned features are in fact more robust than those of other unsupervised algorithms (Fig. 6). For completeness, we also include the hard WTA network, which is essentially a special case of SoftHebb with very high base in the softmax (Section 2.7), and is therefore learning equally robust features.
|
Why do you only compare the robustness of their model with that of MLPs, rather than with other unsupervised learning approaches?
|
We have included an additional Appendix E with new experiments on adversarial robustness. Section E.2 compares the adversarial robustness of SoftHebb with that of k-means and PCA. SoftHebb is more robust than both alternatives.
|
null | false
| 5
|
Presenting pronunciation data in several languages to the network might create problems because different languages have different pronunciation patterns. For example, the string `real' is pronounced differently in English, German, Spanish, and Portuguese. We solve this problem by prepending each grapheme sequence with an artificial token consisting of the language's ISO 639-3 code enclosed in angle brackets. The English word `real', for example, would be presented to the system as
INLINEFORM0 eng INLINEFORM1 r e a l
The artificial token is treated simply as an element of the grapheme sequence. This is similar to the approach taken by johnson2016google in their zero-shot NMT system. However, their source-side artificial tokens identify the target language, whereas ours identify the source language. An alternative approach, used by ostling2017continuous, would be to concatenate a language embedding to the input at each time step. They do not evaluate their approach on grapheme-to-phoneme conversion.
Presenting pronunciation data in several languages to the network might create problems because different languages have different pronunciation patterns. For example, the string `real' is pronounced differently in English, German, Spanish, and Portuguese. We solve this problem by prepending each grapheme sequence with an artificial token consisting of the language's ISO 639-3 code enclosed in angle brackets. The English word `real', for example, would be presented to the system as
INLINEFORM0 eng INLINEFORM1 r e a l
The artificial token is treated simply as an element of the grapheme sequence. This is similar to the approach taken by johnson2016google in their zero-shot NMT system. However, their source-side artificial tokens identify the target language, whereas ours identify the source language. An alternative approach, used by ostling2017continuous, would be to concatenate a language embedding to the input at each time step. They do not evaluate their approach on grapheme-to-phoneme conversion.
Sometimes this cleaning algorithm works well: in the German examples in Table 3, the raw German symbols /X/ and /?/ are both converted to /x/.****However, the cleaning algorithm can also have negative effects on the data quality.
|
Does the cleaning algorithm work well?
|
Not always.
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null | false
| null |
If Newton's first law says an object in motion will stay in motion unless acted on by an outside force, why does my spinning top stop spinning?
|
Your spinning top stops spinning because it is being acted on by outside forces. Specifically, your spinning top is losing energy due to friction with the ground and air. That energy is being lost to sound and heat as your spinning top pushes against the air in the room and rubs against the floor.
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null | false
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As of 2021, the power and capacity of the largest individual battery storage power plants is an order of magnitude less than that of the largest pumped storage power plants, the most common form of grid energy storage. For example, the Bath County Pumped Storage Station, the second largest in the world, can store 24GWh of electricity and dispatch 3GW while the first phase of Vistra Energy's Moss Landing Energy Storage Facility can store 1.2GWh and dispatch 300MW. Grid batteries do not however have to be large, and smaller ones can be deployed widely across a grid for greater redundancy.
As of 2019, battery power storage is cheaper than open cycle gas turbine power for use up to two hours, and there was around 365 GWh of battery storage deployed worldwide, growing extremely rapidly. Levelized cost of electricity from battery storage has fallen rapidly, halving in two years to US$150 per MWh as of 2020.
|
Given these paragraphs about battery storage power stations, what was the levelized cost of electricity from battery storage as of 2020?
|
US$150 per MWh.
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null | false
| null |
Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, responding to stimuli, providing structure to cells and organisms, and transporting molecules from one location to another. Proteins differ from one another primarily in their sequence of amino acids, which is dictated by the nucleotide sequence of their genes, and which usually results in protein folding into a specific 3D structure that determines its activity.
|
Extract from the text what are the Proteins functions in the organism, and separate them with a comma,
|
catalysing metabolic reactions, DNA replication, responding to stimuli, providing structure to cells and organisms, transporting molecules from one location to another
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null | false
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What is the best wideangle lens for sony apec camera
|
Sigma DC DN 16MM 1.4 . The picture quality is so sharp and the focus is fast.
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null | false
| null |
What are the 7 Wonders of the Solar System:
|
These are 7 wonders that can be found in the Solar System:
- Enceladus, a moon of Saturn
- The Great Red Spot of Jupiter, a massive and persistent anticyclonic storm in the planet's southern hemisphere
- The asteroid belt, a region of innumerable small solid bodies located between the orbits of Mars and Jupiter
- The surface of the Sun
- The oceans of Earth
- The Rings of Saturn
- Olympus Mons, an enormous shield volcano on Mars and the tallest planetary mountain in the Solar System
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null | false
| null |
What are some important things that have happened on the Day April 6?
|
- In 1652 the settlement that later became Cape Town was established
- In 1945, the Battle of Slater's Knoll of the Second World War happened
- In 2009, a 6.3M earthquake hit a town in Italy
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|
2003.00639
| false
| null |
We perform experiments using the following state-of-the-art models: (1) SEQ2SEQ: a sequence-to-sequence model with attention mechanisms BIBREF21, (2) CVAE: a conditional variational auto-encoder model with KL-annealing and a BOW loss BIBREF2, (3) Transformer: an encoder-decoder architecture relying solely on attention mechanisms BIBREF22, (4) HRED: a generalized sequence-to-sequence model with the hierarchical RNN encoder BIBREF23, (5) DialogWAE: a conditional Wasserstein auto-encoder, which models the distribution of data by training a GAN within the latent variable space BIBREF6. We adopt several standard metrics widely used in existing works to measure the performance of dialogue generation models, including BLEU BIBREF24, embedding-based metrics (Average, Extrema, Greedy and Coherence) BIBREF25, BIBREF26, entropy-based metrics (Ent-{1,2}) BIBREF0 and distinct metrics (Dist-{1,2,3} and Intra-{1,2,3}) BIBREF1, BIBREF6.
We perform experiments using the following state-of-the-art models: (1) SEQ2SEQ: a sequence-to-sequence model with attention mechanisms BIBREF21, (2) CVAE: a conditional variational auto-encoder model with KL-annealing and a BOW loss BIBREF2, (3) Transformer: an encoder-decoder architecture relying solely on attention mechanisms BIBREF22, (4) HRED: a generalized sequence-to-sequence model with the hierarchical RNN encoder BIBREF23, (5) DialogWAE: a conditional Wasserstein auto-encoder, which models the distribution of data by training a GAN within the latent variable space BIBREF6.
|
What state of the art models were used in experiments?
|
The answers are shown as follows:
* SEQ2SEQ
* CVAE
* Transformer
* HRED
* DialogWAE
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null | false
| null |
Korematsu v. United States, 323 U.S. 214 (1944), was a landmark decision by the Supreme Court of the United States to uphold the exclusion of Japanese Americans from the West Coast Military Area during World War II. The decision has been widely criticized, with some scholars describing it as "an odious and discredited artifact of popular bigotry", and as "a stain on American jurisprudence". The case is often cited as one of the worst Supreme Court decisions of all time. Chief Justice John Roberts repudiated the Korematsu decision in his majority opinion in the 2018 case of Trump v. Hawaii.
In the aftermath of Imperial Japan's attack on Pearl Harbor, President Franklin D. Roosevelt had issued Executive Order 9066 on February 19, 1942, authorizing the U.S. War Department to create military areas from which any or all Americans might be excluded. Subsequently, the Western Defense Command, a U.S. Army military command charged with coordinating the defense of the West Coast of the United States, ordered "all persons of Japanese ancestry, including aliens and non-aliens" to relocate to internment camps. However, a 23-year-old Japanese-American man, Fred Korematsu, refused to leave the exclusion zone and instead challenged the order on the grounds that it violated the Fifth Amendment.
In a majority opinion joined by five other justices, Associate Justice Hugo Black held that the need to protect against espionage by Japan outweighed the rights of Americans of Japanese ancestry. Black wrote that "Korematsu was not excluded from the Military Area because of hostility to him or his race", but rather "because the properly constituted military authorities ... decided that the military urgency of the situation demanded that all citizens of Japanese ancestry be segregated from the West Coast" during the war against Japan. Dissenting justices Frank Murphy, Robert H. Jackson, and Owen J. Roberts all criticized the exclusion as racially discriminatory; Murphy wrote that the exclusion of Japanese "falls into the ugly abyss of racism" and resembled "the abhorrent and despicable treatment of minority groups by the dictatorial tyrannies which this nation is now pledged to destroy."
|
Can you summarize the Korematsu case?
|
Korematsu v United States was a Supreme Court decision that upheld the removal of citizens of Japanese descent from the West Coast of the US during WWII. The decision has been widely criticized and is often cited as one of the worst Supreme Court decisions in US history. The case was repudiated by the court in 2018.
The case was brought by Fred Korematsu, who refused to move and challenged the order on the grounds that it violated the Fifth Amendment.
The majority held that the need to protect the US against espionage by Japan outweighed the individual rights of Japanese-American citizens during wartime. Three dissenting justices argued that the removal was racially discriminatory.
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null | false
| null |
Give me a bulleted list of 5 hobbies I should try.
|
Sure, I'd be happy to give you a bulleted list of 5 hobbies you should try.
- Pickle ball
- Woodworking
- Painting
- Biking
- Crocheting
I hope you like the 5 suggestions above and try a new hobby!
|
|
null | false
| null |
Tell me which of the following numbers are whole numbers and which are integers: -9, -8, -7, -6, -5, -4, −3, −2, −1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
|
Whole numbers include all positive numbers and 0: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
Integers are numbers that include positive and negative numbers and 0: -9, -8, -7, -6, -5, -4, −3, −2, −1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
Whole numbers are a subset of Integers.
|
|
null | false
| null |
Tell me about the most amazing trip an American could take.
|
Many Americans could experience amazing trips from just traveling within the United States. However, the most amazing trip might consist of traveling outside the United States and heading to a country they have never been to. For instance, a trip to Thailand in Asia or Spain in Europe might make for an amazing experience due to being able to experience new cultures, see completely new sites, and taste incredible local cuisines.
|
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null | false
| null |
Samuel Wesley (24 February 1766 – 11 October 1837) was an English organist and composer in the late Georgian period. Wesley was a contemporary of Mozart (1756–1791) and was called by some "the English Mozart".
Born in Bristol, he was the son of noted Methodist and hymnodist Charles Wesley, the grandson of Samuel Wesley (a poet of the late Stuart period) and the nephew of John Wesley, the founder of the Methodist Church.
|
Who was the founder of the Methodist Church?
|
John Wesley
|
null | false
| null |
Why is pickleball popular?
|
Pickleball has become popular because the equipment (paddles and balls) is cheap and existing tennis courts are easily converted. Pickleball is easily learned without lessons. There are social benefits to pickleball due to small courts that tend to promote conversation.
|
|
2004.02451
| false
| null |
We first inspect the frequencies of object and subject RCs in the training data, by parsing them with the state-of-the-art Berkeley neural parser BIBREF19. In total, while subject RCs occur 373,186 times, object RCs only occur 106,558 times. We create three additional training datasets by adding sentences involving object RCs to the original Wikipedia corpus (Section lm). To this end, we randomly pick up 30 million sentences from Wikipedia (not overlapped to any sentences in the original corpus), parse by the same parser, and filter sentences containing an object RC, amounting to 680,000 sentences. Among the test cases about object RCs, we compare accuracies on subject-verb agreement, to make a comparison with subject RCs. We also evaluate on “animate only” subset, which has a correspondence to the test cases for subject RC with only differences in word order and inflection (like (UNKREF45) and (UNKREF46); see footnote FOOTREF47). Of particular interest to us is the accuracy on these animate cases. Since the vocabularies are exactly the same, we hypothesize that the accuracy will reach the same level as that on subject RCs with our augmentation.
We create three additional training datasets by adding sentences involving object RCs to the original Wikipedia corpus (Section lm). To this end, we randomly pick up 30 million sentences from Wikipedia (not overlapped to any sentences in the original corpus), parse by the same parser, and filter sentences containing an object RC, amounting to 680,000 sentences.
|
How do they perform data augmentation?
|
They randomly sample sentences from Wikipedia that contains an object RC and add them to training data
|
null | false
| null |
Which product is part of the Security & Governance model in Lakehouse and which is used for Datawarehouse in Delta Lake? Unity Catalog, SQL endpoint, Serverless SQL, Storage credential.
|
Unity Catalog and Storage credential is part of the Security & Governance model in Lakehouse . SQL endpoint and Serverless SQL is used for Datawarehouse in Delta Lake.
|
|
null | false
| null |
Who are the best basketball players of all time?
|
1) Micheal Jordan
2) LeBron James
3) Bill Russell
4) Magic Johnson
5) Larry Bird
6) Kareem Abdul Jabbar
7) Stephen Curry
8) Kobe Bryant
9) Shaquille O'Neal
10) Tim Duncan
|
|
null | false
| null |
The Republican Party of Arkansas (RPA), headquartered at 1201 West 6th Street in downtown Little Rock, is the affiliate of the Republican Party in Arkansas. It is currently the dominant party in the state, controlling all four of Arkansas' U.S. House seats, both U.S. Senate seats, all statewide executive offices, including the governorship, and supermajorities in both houses of the state legislature.
|
List down some facts regarding the Republican Party of Arkansas from given text
|
1. The Republican Party in Arkansas is affiliated with the Republican Party of Arkansas (RPA), which has its headquarters at 1201 West 6th Street in downtown Little Rock.
2. It currently holds the majority in the state.
3. Possessing supermajorities in both houses of the state legislature and control of all four U.S. House seats, both U.S. Senate seats, all statewide executive positions, and the governorship.
|
null | false
| null |
Which of the following words are considered a palindrome, a word that can be spelled the same forwards and backward: deified, kayak, chicken, taco, wow, peep, noon, abba, lizard, bob, penguin, margarita, square
|
deified, kayak, wow, peep, noon, abba, bob
|
|
null | false
| null |
Which one of these do not belong to the group: Tomato, Carrot, Ginger, Potato, Turmeric
|
All except Tomato grow underground.
|
|
null | false
| null |
What are drop bears?
|
Drop bears are carnivorous version of the Koala bear from Australian folklore. Stories of drop bears are typically used by locals to scare tourists.
|
|
null | false
| null |
What is Collateral?
|
If there is a main security for a debt, such as a house securing a mortgage, any extra security supplied is called collateral.
|
|
null | false
| null |
Which is better - skiing or snowboarding
|
I prefer skiing over snowboarding. For three reasons: safety, reliability, and expediency
Safety - snowboarding can be a very dangerous sport. I'd been skiing and snowboarding for years. One winter I had a snowboarding accident and slid into a tree. Snowboarding requires larger turns with a bigger radius. I wasn't able to turn in time to avoid the tree.
Reliability - The thing about being on only a single piece of equipment is that your balance relies much more on the snow conditions. Under ideal conditions, its great to be able to snowboard and maintain control with the edge. Skiing on powder is also a dream. However, if the conditions are less than ideal, then snowboarding on ice becomes a dangerous, out of control situation. Skiing on ice is also no picnic, but at least you can stabilize and balance on your two skis.
Expediency - Skiing with friends inevitably means that you are waiting for the slowest one before you can start down the mountain. I was that friend. Snowboarders have to sit down and strap in, while skiers can pretty much get right off the lift and start down the mountain immediately. This makes mixed groups of skiers and snowboarders the trickiest. So I would sometimes snowboard if I was traveling with snowboarders, and ski if I was traveling with skiers.
|
|
null | false
| null |
Who was the first female to swim the English Channel?
|
In 1926, Gertrude Ederle was the first woman to swim the English Channel. She was only 19 years old and an Olympic medalist when she made the crossing.
|
|
null | false
| null |
Give me three reasons why cactuses aren't real.
|
My first point is that rich people make cactuses to prevent the prevention of desertification, which is a weird statement in itself. This leads to more deserts, and therefore more oil, and more money for rich people.
Secondly, everyone I know with a cactus hasn't watered it in about eighteen centuries and somehow it is still quote unquote "alive." Shallow roots, really? You never water it ever, it is not real, admit it.
Third and finally, two thousand species of cacti is just too many. It's really because rich people aren't very good at creating the same cactuses twice, so we end up with all of their bespoke versions.
Thank you for coming to my TED Talk.
|
|
2002.11893
| false
| null |
Model: We adapted BERTNLU from ConvLab-2. BERT BIBREF22 has shown strong performance in many NLP tasks. We use Chinese pre-trained BERT BIBREF23 for initialization and then fine-tune the parameters on CrossWOZ. We obtain word embeddings and the sentence representation (embedding of [CLS]) from BERT. Since there may exist more than one intent in an utterance, we modify the traditional method accordingly. For dialogue acts of inform and recommend intents such as (intent=Inform, domain=Attraction, slot=fee, value=free) whose values appear in the sentence, we perform sequential labeling using an MLP which takes word embeddings ("free") as input and outputs tags in BIO schema ("B-Inform-Attraction-fee"). For each of the other dialogue acts (e.g., (intent=Request, domain=Attraction, slot=fee)) that do not have actual values, we use another MLP to perform binary classification on the sentence representation to predict whether the sentence should be labeled with this dialogue act. To incorporate context information, we use the same BERT to get the embedding of last three utterances. We separate the utterances with [SEP] tokens and insert a [CLS] token at the beginning. Then each original input of the two MLP is concatenated with the context embedding (embedding of [CLS]), serving as the new input. We also conducted an ablation test by removing context information. We trained models with both system-side and user-side utterances.
Model: We implemented a rule-based model (RuleDST) and adapted TRADE (Transferable Dialogue State Generator) BIBREF19 in this experiment. RuleDST takes as input the previous system state and the last user dialogue acts. Then, the system state is updated according to hand-crafted rules. For example, If one of user dialogue acts is (intent=Inform, domain=Attraction, slot=fee, value=free), then the value of the "fee" slot in the attraction domain will be filled with "free". TRADE generates the system state directly from all the previous utterances using a copy mechanism. As mentioned in Section SECREF18, the first query of the system often records full user constraints, while the last one records relaxed constraints for recommendation. Thus the last one involves system policy, which is out of the scope of state tracking. We used the first query for these models and left state tracking with recommendation for future work.
Model: We adapted a vanilla policy trained in a supervised fashion from ConvLab-2 (SL policy). The state $s$ consists of the last system dialogue acts, last user dialogue acts, system state of the current turn, the number of entities that satisfy the constraints in the current domain, and a terminal signal indicating whether the user goal is completed. The action $a$ is delexicalized dialogue acts of current turn which ignores the exact values of the slots, where the values will be filled back after prediction.
We adapted BERTNLU from ConvLab-2.
We implemented a rule-based model (RuleDST) and adapted TRADE (Transferable Dialogue State Generator) BIBREF19 in this experiment.
We adapted a vanilla policy trained in a supervised fashion from ConvLab-2 (SL policy).
|
What are the benchmark models?
|
The answers are shown as follows:
* BERTNLU from ConvLab-2
* a rule-based model (RuleDST)
* TRADE (Transferable Dialogue State Generator)
* a vanilla policy trained in a supervised fashion from ConvLab-2 (SL policy)
|
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