Subset (2)

qa · 3.63k rows

Split (2)

train · 2.32k rows

id stringlengths 1 4	example_id stringlengths 1 3	question stringlengths 12 167	context listlengths 1 168	answer stringlengths 0 1.61k	choices null	question_type stringclasses 3 values
0	0	What is the seed lexicon?	[ "Experiments ::: Results and Discussion\t Table TABREF23 shows accuracy. As the Random baseline suggests, positive and negative labels were distributed evenly. The Random+Seed baseline made use of the seed lexicon and output the corresponding label (or the reverse of it for negation) if the event's predicate is in ...	a vocabulary of positive and negative predicates that helps determine the polarity score of an event	null	free_form
1	0	What are the results?	[ "This suggests that the training set of 0.6 million events is sufficiently large for training the models. For comparison, we trained the models with a subset (6,000 events) of the ACP dataset. As the results shown in Table TABREF24 demonstrate, our method is effective when labeled data are small. The result of hype...	Using all data to train: AL -- BiGRU achieved 0.843 accuracy, AL -- BERT achieved 0.863 accuracy, AL+CA+CO -- BiGRU achieved 0.866 accuracy, AL+CA+CO -- BERT achieved 0.835, accuracy, ACP -- BiGRU achieved 0.919 accuracy, ACP -- BERT achived 0.933, accuracy, ACP+AL+CA+CO -- BiGRU achieved 0.917 accuracy, ACP+AL+CA+CO -...	null	free_form
2	0	How are relations used to propagate polarity?	[ "Related Work\tLearning affective events is closely related to sentiment analysis. Whereas sentiment analysis usually focuses on the polarity of what are described (e.g., movies), we work on how people are typically affected by events. In sentiment analysis, much attention has been paid to compositionality. Word-le...	based on the relation between events, the suggested polarity of one event can determine the possible polarity of the other event	null	free_form
3	0	How big is the Japanese data?	[ "Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network mod...	7000000 pairs of events were extracted from the Japanese Web corpus, 529850 pairs of events were extracted from the ACP corpus	null	free_form
4	0	What are labels available in dataset for supervision?	[ "Experiments ::: Results and Discussion\t Table TABREF23 shows accuracy. As the Random baseline suggests, positive and negative labels were distributed evenly. The Random+Seed baseline made use of the seed lexicon and output the corresponding label (or the reverse of it for negation) if the event's predicate is in ...	negative, positive	null	extractive
5	0	How big are improvements of supervszed learning results trained on smalled labeled data enhanced with proposed approach copared to basic approach?	[ "Minimally Supervised Learning of Affective Events Using Discourse Relations\tRecognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable...	3%	null	free_form
6	0	How does their model learn using mostly raw data?	[ "Minimally Supervised Learning of Affective Events Using Discourse Relations\tRecognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable...	by exploiting discourse relations to propagate polarity from seed predicates to final sentiment polarity	null	free_form
7	0	How big is seed lexicon used for training?	[ "Experiments ::: Results and Discussion\t Table TABREF23 shows accuracy. As the Random baseline suggests, positive and negative labels were distributed evenly. The Random+Seed baseline made use of the seed lexicon and output the corresponding label (or the reverse of it for negation) if the event's predicate is in ...	30 words	null	free_form
8	0	How large is raw corpus used for training?	[ "Compared with this method, our discourse relation-based linking of events is much simpler and more intuitive. Some previous studies made use of document structure to understand the sentiment. proposed a sentiment-specific pre-training strategy using unlabeled dialog data (tweet-reply pairs). proposed a method of...	100 million sentences	null	extractive
9	1	Does the paper report macro F1?	[ "The best model overall is DBMDZ (.520), showing a balanced response on both validation and test set. See Table TABREF37 for a breakdown of all emotions as predicted by the this model. Precision is mostly higher than recall. The labels Awe/Sublime, Suspense and Humor are harder to predict than the other labels. The...	Yes	null	yes_no
10	1	How is the annotation experiment evaluated?	[ "Thus, we conceptualize a set of aesthetic emotions that are predictive of aesthetic appreciation in the reader, and allow the annotation of multiple labels per line to capture mixed emotions within context. We evaluate this novel setting in an annotation experiment both with carefully trained experts and via crowd...	confusion matrices of labels between annotators	null	extractive
11	1	What are the aesthetic emotions formalized?	[ "PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry\tMost approaches to emotion analysis regarding social media, literature, news, and other domains focus exclusively on basic emotion categories as defined by Ekman or Plutchik. However, art (such as literature) en...	feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking), Emotions that exhibit this dual capacity have been defined as “aesthetic emotions”	null	extractive
12	2	Do they report results only on English data?	[ "We compute a basic measure of the acculturation gap for a community-month INLINEFORM0 as the relative difference of the cross-entropy of comments by users active in INLINEFORM1 with that of singleton comments by outsiders—i.e., users who only ever commented once in INLINEFORM2 , but who are still active in Reddit ...	No	null	yes_no
13	2	How do the various social phenomena examined manifest in different types of communities?	[ "More generally, our methodology reveals differences in how various social phenomena manifest across communities, and shows that structuring the multi-community landscape can lead to a better understanding of the systematic nature of this diversity.", "By averaging our measures of distinctiveness and dynamicity a...	Dynamic communities have substantially higher rates of monthly user retention than more stable communities. More distinctive communities exhibit moderately higher monthly retention rates than more generic communities. There is also a strong positive relationship between a community's dynamicity and the average number o...	null	free_form
14	2	What patterns do they observe about how user engagement varies with the characteristics of a community?	[ "While striking patterns of user engagement have been uncovered in prior case studies of individual communities , , , , , we do not know whether these observations hold beyond these cases, or when we can draw analogies between different communities. Are there certain types of communities where we can expect si...	communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members, within distinctive communities, established users have an increased propensity to engage with the community's special...	null	extractive
15	2	How did the select the 300 Reddit communities for comparison?	[ "Community Identity and User Engagement in a Multi-Community Landscape\tA community's identity defines and shapes its internal dynamics. Our current understanding of this interplay is mostly limited to glimpses gathered from isolated studies of individual communities. In this work we provide a systematic exploratio...	They selected all the subreddits from January 2013 to December 2014 with at least 500 words in the vocabulary and at least 4 months of the subreddit's history. They also removed communities with the bulk of the contributions are in foreign language.	null	free_form
16	2	How do the authors measure how temporally dynamic a community is?	[ "While striking patterns of user engagement have been uncovered in prior case studies of individual communities , , , , , we do not know whether these observations hold beyond these cases, or when we can draw analogies between different communities. Are there certain types of communities where we can expect si...	the average volatility of all utterances	null	extractive
17	2	How do the authors measure how distinctive a community is?	[ "We compute a basic measure of the acculturation gap for a community-month INLINEFORM0 as the relative difference of the cross-entropy of comments by users active in INLINEFORM1 with that of singleton comments by outsiders—i.e., users who only ever commented once in INLINEFORM2 , but who are still active in Reddit ...	the average specificity of all utterances	null	extractive
18	3	What data is the language model pretrained on?	[ "Related Work ::: Pre-trained Language Model\tRecently, some works focused on pre-trained language representation models to capture language information from text and then utilizing the information to improve the performance of specific natural language processing tasks , , , which makes language model a shared mo...	Chinese general corpus	null	extractive
19	3	What baselines is the proposed model compared against?	[ "Experimental Studies ::: Comparison with State-of-the-art Methods\tSince BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet ) and BERT-Base . As BERT has two versions: BERT-Base ...	BERT-Base, QANet	null	extractive
20	3	How is the clinical text structuring task defined?	[ "Introduction\tClinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surge...	Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the spe...	null	extractive
21	3	What are the specific tasks being unified?	[ "Introduction\tClinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surge...	three types of questions, namely tumor size, proximal resection margin and distal resection margin	null	extractive
22	3	Is all text in this dataset a question, or are there unrelated sentences in between questions?	[ "Experimental Studies ::: Dataset and Evaluation Metrics\tOur dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and...	the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include additional sentences	null	free_form
23	3	How many questions are in the dataset?	[ "Applying Concatenation first can only achieve 80.74% in EM-score and 84.42% in F$_1$-score. This is probably due to the processing depth of hidden vectors and dataset size. BERT's output has been modified after many layers but named entity information representation is very close to input. With big amount of param...	2,714	null	free_form
24	3	How they introduce domain-specific features into pre-trained language model?	[ "Question Answering based Clinical Text Structuring Using Pre-trained Language Model\tClinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as taskspecific end-to-end models and pipeline models usually suffer from the lack of dataset and error propagation. In t...	integrate clinical named entity information into pre-trained language model	null	extractive
25	3	How big is QA-CTS task dataset?	[ "Conclusion\tIn this paper, we present a question answering based clinical text structuring (QA-CTS) task, which unifies different clinical text structuring tasks and utilize different datasets. A novel model is also proposed to integrate named entity information into a pre-trained language model and adapt it to QA...	17,833 sentences, 826,987 characters and 2,714 question-answer pairs	null	extractive
26	3	How big is dataset of pathology reports collected from Ruijing Hospital?	[ "Question Answering based Clinical Text Structuring Using Pre-trained Language Model\tClinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as taskspecific end-to-end models and pipeline models usually suffer from the lack of dataset and error propagation. In t...	17,833 sentences, 826,987 characters and 2,714 question-answer pairs	null	extractive
27	3	What are strong baseline models in specific tasks?	[ "Question Answering based Clinical Text Structuring Using Pre-trained Language Model\tClinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as taskspecific end-to-end models and pipeline models usually suffer from the lack of dataset and error propagation. In t...	state-of-the-art question answering models (i.e. QANet ) and BERT-Base	null	extractive
28	4	What aspects have been compared between various language models?	[ "Conclusion\tIn the present work, we describe and examine the tradeoff space between quality and performance for the task of language modeling. Specifically, we explore the quality–performance tradeoffs between KN-5, a non-neural approach, and AWD-LSTM and QRNN, two neural language models. We find that with decreas...	Quality measures using perplexity and recall, and performance measured using latency and energy usage.	null	free_form
29	4	what classic language models are mentioned in the paper?	[ "Progress and Tradeoffs in Neural Language Models\tIn recent years, we have witnessed a dramatic shift towards techniques driven by neural networks for a variety of NLP tasks. Undoubtedly, neural language models (NLMs) have reduced perplexity by impressive amounts. This progress, however, comes at a substantial cos...	Kneser–Ney smoothing	null	extractive
30	4	What is a commonly used evaluation metric for language models?	[ "Experimental Setup\tWe conducted our experiments on Penn Treebank (PTB; ) and WikiText-103 (WT103; ). Preprocessed by , PTB contains 887K tokens for training, 70K for validation, and 78K for test, with a vocabulary size of 10,000. On the other hand, WT103 comprises 103 million tokens for training, 217K for vali...	perplexity	null	extractive
31	5	Which dataset do they use a starting point in generating fake reviews?	[ "We will test the effect of these two parameters, the Bernoulli probability $b$ and log-likelihood penalty of including ``forgotten'' words $\\lambda$, with a user study in Section~\\ref{sec:varying}. \\paragraph{Start penalty} We introduce start penalties to avoid generic sentence starts (e.g. ``Great food, grea...	the Yelp Challenge dataset	null	extractive
32	5	Do they use a pretrained NMT model to help generating reviews?	[ "We used the same graphics card (GeForce GTX) and trained using the same framework (torch-RNN in lua). We downloaded the reviews from Yelp Challenge and preprocessed the data to only contain printable ASCII characters, and filtered out non-restaurant reviews. We trained the model for approximately 72 hours. We post...	No	null	yes_no
33	5	What kind of model do they use for detection?	[ "\\newblock In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, ACM (2017) \\bibitem{murphy2012machine} Murphy, K.: \\newblock Machine learning: a probabilistic approach. \\newblock Massachusetts Institute of Technology (2012) \\bibitem{challenge2013yelp} Yelp: \\newblock...	AdaBoost-based classifier	null	extractive
34	5	Does their detection tool work better than human detection?	[ "The authors investigated the use of NMT models in chatbot systems. We found that unigram penalties to random tokens (Algorithm~\\ref{alg:aug}) was easy to implement and produced sufficiently diverse responses. \\section {Discussion and Future Work} \\paragraph{What makes NMT-Fake* reviews difficult to detect?}...	Yes	null	yes_no
35	5	How many reviews in total (both generated and true) do they evaluate on Amazon Mechanical Turk?	[ "\\subsection{Experiment: Varying generation parameters in our NMT model} \\label{sec:varying} Parameters $b$ and $\\lambda$ control different aspects in fake reviews. We show six different examples of generated fake reviews in Table~\\ref{table:categories}. Here, the largest differences occur with increasing val...	1,006 fake reviews and 994 real reviews	null	extractive
36	6	Which baselines did they compare?	[ "Future work would look into the saliency maps generated by applying LRP to pointer-generator networks and compare to our current results as well as mathematically justifying the average that we did when validating our saliency maps. Some additional work is also needed on the validation of the saliency maps with co...	The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in and we use a greedy search for decoding. We train end-to-end i...	null	extractive
37	6	How many attention layers are there in their model?	[ "Layer-Wise Relevance Propagation\tWe present in this section the Layer-Wise Relevance Propagation (LRP) technique that we used to attribute importance to the input features, together with how we adapted it to our model and how we generated the saliency maps. LRP redistributes the output of the model from the outp...	one	null	free_form
38	6	Is the explanation from saliency map correct?	[ "Introduction\tEver since the LIME algorithm , \"explanation\" techniques focusing on finding the importance of input features in regard of a specific prediction have soared and we now have many ways of finding saliency maps (also called heat-maps because of the way we like to visualize them). We are interested in...	No	null	yes_no
39	7	How is embedding quality assessed?	[ "Probabilistic Bias Mitigation in Word Embeddings\tIt has been shown that word embeddings derived from large corpora tend to incorporate biases present in their training data. Various methods for mitigating these biases have been proposed, but recent work has demonstrated that these methods hide but fail to truly r...	We compare this method of bias mitigation with the no bias mitigation ("Orig"), geometric bias mitigation ("Geo"), the two pieces of our method alone ("Prob" and "KNN") and the composite method ("KNN+Prob"). We note that the composite method performs reasonably well according the the RIPA metric, and much better than t...	null	extractive
40	7	What are the three measures of bias which are reduced in experiments?	[ "Probabilistic Bias Mitigation in Word Embeddings\tIt has been shown that word embeddings derived from large corpora tend to incorporate biases present in their training data. Various methods for mitigating these biases have been proposed, but recent work has demonstrated that these methods hide but fail to truly r...	RIPA, Neighborhood Metric, WEAT	null	free_form
41	8	What turn out to be more important high volume or high quality data?	[ "By carefully gathering high-quality data and optimising the models to the characteristics of each language, we deliver embeddings with correlations of $\\rho $=$0.39$ (Yorùbá) and $\\rho $=$0.44$ (Twi) on the same test set, still far from the high-resourced models, but representing an improvement over $170\\%$ on ...	only high-quality data helps	null	extractive
42	8	What two architectures are used?	[ "cwe also proposed three alternatives to learn multiple embeddings per character and resolve ambiguities: (i) position-based character embeddings where each character has different embeddings depending on the position it appears in a word, i.e., beginning, middle or end (ii) cluster-based character embeddings where...	fastText, CWE-LP	null	extractive
43	9	What were the word embeddings trained on?	[ "Portuguese Embedding\tIn , the quality of the representation of words through vectors in several models is discussed. According to the authors, the ability to train high-quality models using simplified architectures is useful in models composed of predictive methods that try to predict neighboring words with one ...	large Portuguese corpus	null	extractive
44	9	Which word embeddings are analysed?	[ "Related Work\tThere is a wide range of techniques that provide interesting results in the context of ML algorithms geared to the classification of data without discrimination; these techniques range from the pre-processing of data to the use of bias removal techniques in fact. Approaches linked to the data pre-p...	Continuous Bag-of-Words (CBOW)	null	extractive
45	10	Did they experiment on this dataset?	[ "Discussion\tThis paper introduced the first dataset of citation data of the three Czech apex courts. Understandably, there are some pitfalls and limitations to our approach. As we admitted in the evaluation in Section SECREF9, the models we included in our NLP pipelines are far from perfect. Overall, we were able ...	No	null	yes_no
46	10	How is quality of the citation measured?	[ "Citation Data of Czech Apex Courts\tIn this paper, we introduce the citation data of the Czech apex courts (Supreme Court, Supreme Administrative Court and Constitutional Court). This dataset was automatically extracted from the corpus of texts of Czech court decisions - CzCDC 1.0. We obtained the citation data by...	it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher ...	null	extractive
47	10	How big is the dataset?	[ "Conclusion\tIn this paper, we have described the process of the creation of the first dataset of citation data of the three Czech apex courts. The dataset is publicly available for download at https://github.com/czech-case-law-relevance/czech-court-citations-dataset.", "Methodology\tIn this paper, we present and...	903019 references	null	free_form
48	11	Do the authors mention any possible confounds in this study?	[ "Several existing researches showed that, twitter posts of war veterans could be a significant indicator of their mental health and could be utilized to predict PTSD sufferers in time before going out of control , , , , , , . However, all of the proposed methods relied on either blackbox machine learning methods or...	No	null	yes_no
49	11	How is the intensity of the PTSD established?	[ "In future, we aim to collect more data and run not only nationwide but also international-wide data collection to establish our innovation into a real tool. Apart from that, as we achieved promising results in detecting PTSD and its intensity using only twitter data, we aim to develop Linguistic Dictionary for oth...	Given we have four intensity, No PTSD, Low Risk PTSD, Moderate Risk PTSD and High Risk PTSD with a score of 0, 1, 2 and 3 respectively, the estimated intensity is established as mean squared error.	null	free_form
50	11	How is LIWC incorporated into this system?	[ "LAXARY: Explainable PTSD Detection Model ::: Feature Extraction and Survey Score Estimation\tWe use the exact similar method of LIWC to extract $\\alpha $-scores for each dimension and categories except we use our generated PTSD Linguistic Dictionary for the task . Thus we have total 16 $\\alpha $-scores in total....	For each user, we calculate the proportion of tweets scored positively by each LIWC category.	null	extractive
51	11	How many twitter users are surveyed using the clinically validated survey?	[ "Among these responses, 92 users were diagnosed as PTSD by any of the three surveys and rest of the 118 users are diagnosed with NO PTSD. Among the clinically diagnosed PTSD sufferers, 17 of them were not self-identified before. However, 7 of the self-identified PTSD sufferers are assessed with no PTSD by PTSD asse...	210	null	extractive
52	11	Which clinically validated survey tools are used?	[ "Challenges and Future Work\tLAXARY is a highly ambitious model that targets to fill up clinically validated survey tools using only twitter posts. Unlike the previous twitter based mental health assessment tools, LAXARY provides a clinically interpretable model which can provide better classification accuracy and ...	DOSPERT, BSSS and VIAS	null	extractive
53	12	Did they experiment with the dataset?	[ "Conclusion\tIn the future, we will further improve the CORD-19-NER dataset quality. We will also build text mining systems based on the CORD-19-NER dataset with richer functionalities. We hope this dataset can help the text mining community build downstream applications. We also hope this dataset can bring insight...	Yes	null	yes_no
54	12	What is the size of this dataset?	[ "Conclusion\tIn the future, we will further improve the CORD-19-NER dataset quality. We will also build text mining systems based on the CORD-19-NER dataset with richer functionalities. We hope this dataset can help the text mining community build downstream applications. We also hope this dataset can bring insight...	29,500 documents	null	extractive
55	12	Do they list all the named entity types present?	[ "Viral Protein: Hemagglutinin, GP120, etc. Livestock: cattle, sheep, pig, etc. Wildlife: bats, wild animals, wild birds, etc Evolution: genetic drift, natural selection, mutation rate, etc Physical Science: atomic charge, Amber force fields, Van der Waals interactions, etc. Substrate: blood, sputum, urine, etc. Mat...	No	null	yes_no
56	13	how is quality measured?	[ "Results\tIn Table TABREF13 we compare the quality of UniSent with the Baseline-Lexicon as well as with the gold standard lexicon for general domain data. The results show that (i) UniSent clearly outperforms the baseline for all languages (ii) the quality of UniSent is close to manually annotated data (iii) the do...	Accuracy and the macro-F1 (averaged F1 over positive and negative classes) are used as a measure of quality.	null	free_form
57	13	what sentiment sources do they compare with?	[ "Introduction\tSentiment classification is an important task which requires either word level or document level sentiment annotations. Such resources are available for at most 136 languages , preventing accurate sentiment classification in a low resource setup. Recent research efforts on cross-lingual transfer lea...	manually created lexicon in Czech , German , French , Macedonian , and Spanish	null	extractive
58	14	Is the method described in this work a clustering-based method?	[ "Instead of that, word sense inventories are induced automatically from the clusters, treating each cluster as a single sense of a word. WSI approaches fall into three main groups: context clustering, word ego-network clustering and synonyms (or substitute) clustering. Context clustering approaches consist in creat...	Yes	null	yes_no
59	14	How are the different senses annotated/labeled?	[ "Evaluation ::: Analysis\tIn order to see how the separation of word contexts that we perform corresponds to actual senses of polysemous words, we visualise ego-graphs produced by our method. Figure FIGREF17 shows the nearest neighbours clustering for the word Ruby, which divides the graph into five senses: Ruby-re...	The contexts are manually labelled with WordNet senses of the target words	null	extractive
60	14	Was any extrinsic evaluation carried out?	[ "A fastText model is able to generate a vector for each word even if it is not represented in the vocabulary, due to the use of subword information. However, our system cannot assemble sense vectors for out-of-vocabulary words, for such words it returns their original fastText vector. Still, the coverage of the ben...	Yes	null	yes_no
61	15	Does the model use both spectrogram images and raw waveforms as features?	[ "The model architecture for both the raw waveforms and log-Mel spectrogram images is discussed in Section 3 along with the a discussion on hyperparameter space exploration. In Section 4 we present the experimental results. Finally, in Section 5 we discuss the conclusions drawn from the experiment and future work.",...	No	null	yes_no
62	15	Is the performance compared against a baseline model?	[ "Proposed Method ::: Model Details: 1D ConvNet ::: Hyperparameter Optimization:\tTuning hyperparameters is a cumbersome process as the hyperparamter space expands exponentially with the number of parameters, therefore efficient exploration is needed for any feasible study. We used the random search algorithm suppor...	Yes	null	yes_no
63	15	What is the accuracy reported by state-of-the-art methods?	[ "Results and Discussion\tThis paper discusses two end-to-end approaches which achieve state-of-the-art results in both the image as well as audio domain on the VoxForge dataset . In Table TABREF25, we present all the classification accuracies of the two models of the cases with and without mixup for six and four la...	Answer with content missing: (Table 1) Previous state-of-the art on same dataset: ResNet50 89% (6 languages), SVM-HMM 70% (4 languages)	null	free_form
64	16	Which vision-based approaches does this approach outperform?	[ "The two types of features are complementary for word translation. Experimental results on multiple language pairs demonstrate the effectiveness of our proposed method, which substantially outperforms previous vision-based approaches without using any parallel sentences or supervision of seed word pairs.", "The v...	CNN-mean, CNN-avgmax	null	extractive
65	16	What baseline is used for the experimental setup?	[ "Experimental Setup\tFor the multi-lingual caption model, we set the word embedding size and the hidden size of LSTM as 512. Adam algorithm is applied to optimize the model with learning rate of 0.0001 and batch size of 128. The caption model is trained up to 100 epochs and the best model is selected according to c...	CNN-mean, CNN-avgmax	null	extractive
66	16	Which languages are used in the multi-lingual caption model?	[ "Similarly, INLINEFORM0 is the probability of generating INLINEFORM1 in the target language, which shares INLINEFORM2 with the source language. By sharing the same parameters across different languages in the encoder and decoder, both the visual features and the learned word embeddings for different languages are e...	German-English, French-English, and Japanese-English	null	extractive
67	17	Did they experiment on all the tasks?	[ "We fine-tune BERT on each respective labeled dataset for each task. For BERT input, we apply WordPiece tokenization, setting the maximal sequence length to 50 words/WordPieces. For all tasks, we use a TensorFlow implementation. An exception is the sentiment analysis task, where we used a PyTorch implementation wit...	Yes	null	yes_no
68	17	What models did they compare to?	[ "Introduction ::: \tFor Arabic, a collection of languages and varieties spoken by a wide population of $\\sim 400$ million native speakers covering a vast geographical region (shown in Figure FIGREF2), no such suite of tools currently exists. Many works have focused on sentiment analysis, e.g., , , , , , , , and d...	we do not explicitly compare to previous research since most existing works either exploit smaller data (and so it will not be a fair comparison), use methods pre-dating BERT (and so will likely be outperformed by our models)	null	extractive
69	17	What datasets are used in training?	[ "Data and Models ::: Emotion\tWe make use of two datasets, the LAMA-DINA dataset from , a Twitter dataset with a combination of gold labels from and distant supervision labels. The tweets are labeled with the Plutchik 8 primary emotions from the set: {anger, anticipation, disgust, fear, joy, sadness, surprise, tru...	Arap-Tweet , an in-house Twitter dataset for gender, the MADAR shared task 2 , the LAMA-DINA dataset from , LAMA-DIST, Arabic tweets released by IDAT@FIRE2019 shared-task , , , , , , , , , , ,	null	extractive
70	18	Which GAN do they use?	[ "We do not further distinguish the categories because there is no clear distinction between the three original corpora, “politics,” “world,” and “US.” The results are shown in Figure 3. We observe that the original and artificial examples are generally mixed together and not well separable, which means that the art...	We construct a GAN model which combines different sets of word embeddings INLINEFORM4 , INLINEFORM5 , into a single set of word embeddings INLINEFORM6 .	null	extractive
71	18	Do they evaluate grammaticality of generated text?	[ "Similar models can be found in Odena (2016), the CatGAN in Springenberg (2016), and the LSGAN in Mao et al. (2017). However, all these models consider only images and do not produce word or document embeddings, therefore being different from our models. For generating real text, Zhang et al. (2016) proposed textGA...	No	null	yes_no
72	18	Which corpora do they use?	[ "We do not further distinguish the categories because there is no clear distinction between the three original corpora, “politics,” “world,” and “US.” The results are shown in Figure 3. We observe that the original and artificial examples are generally mixed together and not well separable, which means that the art...	CNN, TIME, 20 Newsgroups, and Reuters-21578	null	extractive
73	19	Do they report results only on English datasets?	[ "Experiments ::: Results on Sentiment Classification from Incorrect Text\tExperimental results for the Twitter Sentiment Classification task on Kaggle's Sentiment140 Corpus dataset, displayed in Table TABREF37, show that our model has better F1-micros scores, outperforming the baseline models by 6$\\%$ to 8$\\%$. W...	Yes	null	yes_no
74	19	How do the authors define or exemplify 'incorrect words'?	[ "Experiments ::: Baseline models ::: Semantic hashing with classifier\tShridhar et al. proposed a word embedding method that doesn't suffer from out-of-vocabulary issues. The authors achieve this by using hash tokens in the alphabet instead of a single word, making it vocabulary independent. For classification, cl...	typos in spellings or ungrammatical words	null	free_form
75	19	Do they test their approach on a dataset without incomplete data?	[ "Stacked DeBERT: All Attention in Incomplete Data for Text Classification\tIn this paper, we propose Stacked DeBERT, short for Stacked Denoising Bidirectional Encoder Representations from Transformers. This novel model improves robustness in incomplete data, when compared to existing systems, by designing a novel e...	No	null	yes_no
76	19	Should their approach be applied only when dealing with incomplete data?	[ "Stacked DeBERT: All Attention in Incomplete Data for Text Classification\tIn this paper, we propose Stacked DeBERT, short for Stacked Denoising Bidirectional Encoder Representations from Transformers. This novel model improves robustness in incomplete data, when compared to existing systems, by designing a novel e...	No	null	yes_no
77	19	By how much do they outperform other models in the sentiment in intent classification tasks?	[ "Experiments ::: Results on Intent Classification from Text with STT Error\tExperimental results for the Intent Classification task on the Chatbot NLU Corpus with STT error can be seen in Table TABREF40. When presented with data containing STT error, our model outperforms all baseline models in both combinations of...	In the sentiment classification task by 6% to 8% and in the intent classification task by 0.94% on average	null	free_form
78	20	What is the sample size of people used to measure user satisfaction?	[ "Analysis ::: Gunrock's Backstory and Persona\tWe assessed the user's interest in Gunrock by tagging instances where the user triggered Gunrock's backstory (e.g., “What's your favorite color?\"). For users with at least one backstory question, we modeled overall (log) Rating with a linear regression by the (log) `N...	34,432 user conversations	null	extractive
79	20	What are all the metrics to measure user engagement?	[ "Analysis ::: Response Depth: Mean Word Count\tTwo unique features of Gunrock are its ability to dissect longer, complex sentences, and its methods to encourage users to be active conversationalists, elaborating on their responses. In prior work, even if users are able to drive the conversation, often bots use simp...	overall rating, mean number of turns	null	extractive
80	20	What the system designs introduced?	[ "Gunrock: A Social Bot for Complex and Engaging Long Conversations\tGunrock is the winner of the 2018 Amazon Alexa Prize, as evaluated by coherence and engagement from both real users and Amazon-selected expert conversationalists. We focus on understanding complex sentences and having in-depth conversations in open...	Amazon Conversational Bot Toolkit, natural language understanding (NLU) (nlu) module, dialog manager, knowledge bases, natural language generation (NLG) (nlg) module, text to speech (TTS) (tts)	null	extractive
81	20	Do they specify the model they use for Gunrock?	[ "System Architecture ::: Dialog Manager\tWe implemented a hierarchical dialog manager, consisting of a high level and low level DMs. The former leverages NLU outputs for each segment and selects the most important segment for the system as the central element using heuristics. For example, “i just finished reading ...	No	null	yes_no
82	20	Do they gather explicit user satisfaction data on Gunrock?	[ "Analysis\tFrom January 5, 2019 to March 5, 2019, we collected conversational data for Gunrock. During this time, no other code updates occurred. We analyzed conversations for Gunrock with at least 3 user turns to avoid conversations triggered by accident. Overall, this resulted in a total of 34,432 user conversati...	Yes	null	yes_no
83	20	How do they correlate user backstory queries to user satisfaction?	[ "Gunrock: A Social Bot for Complex and Engaging Long Conversations\tGunrock is the winner of the 2018 Amazon Alexa Prize, as evaluated by coherence and engagement from both real users and Amazon-selected expert conversationalists. We focus on understanding complex sentences and having in-depth conversations in open...	modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions	null	extractive
84	21	What is the baseline for the experiments?	[ "Experiments ::: Experimental Results\ttab:experimental-results shows the performance of different models on the WNC corpus evaluated on the following four metrics: Precision, Recall, F1, and Accuracy. Our proposed methodology, the use of finetuned optimized BERT based models, and BERT-based ensemble models outperf...	FastText, BiLSTM, BERT	null	extractive
85	21	Which experiments are perfomed?	[ "However, they primarily focused on detecting and mitigating subjective bias for single-word edits. We extend their work by incorporating multi-word edits by detecting bias at the sentence level. We further use their version of the NPOV corpus called Wiki Neutrality Corpus(WNC) for this work. The task of detecting ...	They used BERT-based models to detect subjective language in the WNC corpus	null	free_form
86	22	Is ROUGE their only baseline?	[ "Baseline Metrics\tOur first baseline is ROUGE-L , since it is the most commonly used metric for compression tasks. ROUGE-L measures the similarity of two sentences based on their longest common subsequence. Generated and reference compressions are tokenized and lowercased. For multiple references, we only make us...	No	null	yes_no
87	22	what language models do they use?	[ "In particular, the question has been raised whether the grammatical knowledge that underlies this ability is probabilistic or categorical in nature , , . Within this context, lau2017grammaticality have recently shown that neural language models (LMs) can be used for modeling human ratings of acceptability. Name...	LSTM LMs	null	extractive
88	23	What misbehavior is identified?	[ "An empirical study on the effectiveness of images in Multimodal Neural Machine Translation\tIn state-of-the-art Neural Machine Translation (NMT), an attention mechanism is used during decoding to enhance the translation. At every step, the decoder uses this mechanism to focus on different parts of the source sente...	if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations	null	extractive
89	23	Which attention mechanisms do they compare?	[ "Quantitative results\tWe notice a nice overall progress over CalixtoLC17b multimodal baseline, especially when using the stochastic attention. With improvements of +1.51 BLEU and -2.2 TER on both precision-oriented metrics, the model shows a strong similarity of the n-grams of our candidate translations with resp...	Soft attention, Hard Stochastic attention, Local Attention	null	extractive
90	24	Which paired corpora did they use in the other experiment?	[ "Conclusion\tWe explore a novel way to train a machine commenting model in an unsupervised manner. According to the properties of the task, we propose using the topics to bridge the semantic gap between articles and comments. We introduce a variation topic model to represent the topics, and match the articles and c...	dataset that contains article-comment parallel contents INLINEFORM0 , and an unpaired dataset that contains the documents (articles or comments) INLINEFORM1	null	extractive
91	24	By how much does their system outperform the lexicon-based models?	[ "Unsupervised Machine Commenting with Neural Variational Topic Model\tArticle comments can provide supplementary opinions and facts for readers, thereby increase the attraction and engagement of articles. Therefore, automatically commenting is helpful in improving the activeness of the community, such as online for...	Under the retrieval evaluation setting, their proposed model + IR2 had better MRR than NVDM by 0.3769, better MR by 4.6, and better Recall@10 by 20 . Under the generative evaluation setting the proposed model + IR2 had better BLEU by 0.044 , better CIDEr by 0.033, better ROUGE by 0.032, and better METEOR by 0.029	null	free_form
92	24	Which lexicon-based models did they compare with?	[ "We provide 200 unique comments as the candidate sets, which consists of four types of comments as described in the above retrieval evaluation setting: Correct, Plausible, Popular, and Random. We rank the candidate comment set with four models (TF-IDF, S2S, IR, and Proposed+IR), and record the types of top-1 commen...	TF-IDF, NVDM	null	extractive
93	24	How many comments were used?	[ "Solutions\tFacing the above challenges, we provide three solutions to the problems. Given a large set of candidate comments, the retrieval model can select some comments by matching articles with comments. Compared with the generative model, the retrieval model can achieve more promising performance. First, the re...	from 50K to 4.8M	null	extractive
94	24	How many articles did they have?	[ "However, in the training set, there is only a part of the correct comments, so the other correct comments will be falsely regarded as the negative samples by the supervised model. Therefore, many interesting and informative comments will be discouraged or neglected, because they are not paired with the articles in...	198,112	null	extractive
95	24	What news comment dataset was used?	[ "The comments do not directly tell what happened in the news, but talk about the underlying topics (e.g. NBA Christmas Day games, LeBron James). However, existing methods for machine commenting do not model the topics of articles, which is a potential harm to the generated comments. To this end, we propose an unsup...	Chinese dataset	null	extractive
96	25	By how much do they outperform standard BERT?	[ "Enriching BERT with Knowledge Graph Embeddings for Document Classification\tIn this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata...	up to four percentage points in accuracy	null	extractive
97	25	What dataset do they use?	[ "In particular, Bidirectional Encoder Representations from Transformers (BERT; ) outperformed previous state-of-the-art methods by a large margin on various NLP tasks. We adopt BERT for text-based classification and extend the model with additional metadata provided in the context of the shared task, such as author...	2019 GermEval shared task on hierarchical text classification	null	extractive
98	25	How do they combine text representations with the knowledge graph embeddings?	[ "Enriching BERT with Knowledge Graph Embeddings for Document Classification\tIn this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata...	all three representations are concatenated and passed into a MLP	null	extractive
99	26	What is the algorithm used for the classification tasks?	[ "Diachronic Topics in New High German Poetry\tStatistical topic models are increasingly and popularly used by Digital Humanities scholars to perform distant reading tasks on literary data. It allows us to estimate what people talk about. Especially Latent Dirichlet Allocation (LDA) has shown its usefulness, as it i...	Random Forest Ensemble classifiers	null	extractive

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