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In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
We assess our dataset using traditional and deep learning methods. Our simplest model is a linear SVM trained on word unigrams. SVMs have produced state-of-... | accuracy
| 0 | NIv2 | task460_qasper_answer_generation | fs_opt |
Detailed Instructions: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
See one example below:
Problem: We evaluate the proposed approach on the Chinese social media text summarization task, based on the se... | Questions are gathered from anonymized, aggregated queries to the Google search engine. Queries that are likely to be yes/no questions are heuristically identified: we found selecting queries where the first word is in a manually constructed set of indicator words and are of sufficient length, to be effective. Question... | 4 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example Input: Last, we evaluate our approaches in 9 commonly used text classification datasets. We evaluate our methods on several commonly used datasets ... | Training embeddings from small-corpora can increase the performance of some tasks
| 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
One example is below.
Q: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Larg... | ReviewQA's test set | 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
One example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chin... | intra-sequential and intra-word | 6 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Q: As explained in Section SECREF15 , the corruption introduced in Doc2VecC acts as a data-dependent regularization that suppresses the embeddings of freque... | A unordered text document is one where sentences in the document are disordered or jumbled. It doesn't appear that unordered text documents appear in corpora, but rather are introduced as part of processing pipeline.
****
| 4 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese ... | Solution: English, Spanish and Zulu | 5 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese ... | Solution: Individuals with legal training | 5 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese ... | Solution: BIBREF7 BIBREF26 | 5 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Q: News Articles
Our dataset source of news articles is described in BIBREF2. This dataset was built from two different sources, for the trusted news (real... | three datasets based on IMDB reviews and Yelp reviews
****
| 4 | NIv2 | task460_qasper_answer_generation | fs_opt |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
We evaluate the proposed approach o... | three datasets based on IMDB reviews and Yelp reviews | 0 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Let me give you an example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. ... | Using Latent Dirichlet Allocation on TF-IDF transformed from the corpus | 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Let me give you an example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. ... | On the other hand, phase 1 of Go-Explore finds an optimal trajectory with approximately half the interactions with the environment Moreover, the trajectory length found by Go-Explore is always optimal (i.e. 30 steps) whereas both DQN++ and DRQN++ have an average length of 38 and 42 respectively. Especially interesting... | 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
PROBLEM: We collect three years of online news articles from June 2016 to June 2019.
Question: What unlabeled corpus did they use?
SOLUTI... | No
| 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Each tweet is annotated as no evidence of depression (e.g., “Citizens fear an economic depression") or evidence of depression (e.g., “depressed over disappo... | Introduce a "Refinement Adjustment LSTM-based component" to the decoder
| 0 | NIv2 | task460_qasper_answer_generation | fs_opt |
instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module. We use a triplet ne... | Seq2seq CVAE Hierarchical Gated Fusion Unit (HGFU) Mechanism-Aware Neural Machine (MANM)
| 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Ex Input:
Named Entity Recognition (NER) in the Biomedical domain usually includes recognition of entities such as proteins, genes, diseases, treatments, dr... | [error, correction] pairs
| 1 | NIv2 | task460_qasper_answer_generation | fs_opt |
TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
PROBLEM: We asked medical doctors experienced in extracting knowledge related to medical entities from texts to annotate the entities descri... | PV-DM
| 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese ... | Solution: Introduce a "Refinement Adjustment LSTM-based component" to the decoder | 5 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese ... | Solution: MULTIPLE CHOICE QUESTION ANSWERING | 5 | NIv2 | task460_qasper_answer_generation | fs_opt |
instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
In this work, we introduce a new logical inference engine called MonaLog, which is based on natural logic and work on monotonicity ste... | bilingual dictionary induction, monolingual and cross-lingual word similarity, and cross-lingual hypernym discovery
| 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
Part 1. Definition
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Part 2. Example
We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence m... | Yes | 7 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale C... | To test our proposed category induction model, we consider all BabelNet categories with fewer than 50 known instances. This is motivated by the view that conceptual neighborhood is mostly useful in cases where the number of known instances is small. For each of these categories, we split the set of known instances int... | 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Input: Consider Input: This step entails counting occurrences of all words in the training corpus and sorting them in order of decreasing occurrence. As men... | Output: all annotators that a triple extraction was incorrect
| 2 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale C... | predicting the word given its context | 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Ex Input:
We asked medical doctors experienced in extracting knowledge related to medical entities from texts to annotate the entities described above. Init... | the WMT'14 English-French (En-Fr) and English-German (En-De) datasets.
| 1 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Input: Consider Input: In this work, we develop a technique to rapidly transfer an existing pre-trained model from English to other languages in an energy e... | Output: Feature Concatenation Model (FCM) Spatial Concatenation Model (SCM) Textual Kernels Model (TKM)
| 2 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
One example is below.
Q: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Larg... | The data was collected using 3 components: describe a series of pilot studies that were conducted to collect commonsense inference questions, then discuss the resulting data collection of questions, texts and answers via crowdsourcing on Amazon Mechanical Turk and gives information about some necessary postprocessing s... | 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Let me give you an example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. ... | four different languages: English, Portuguese, Spanish and French | 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Input: Consider Input: Based on annotated corpora and token-based features, studies used machine learning approaches to build word segmentation systems with... | Output: NLG datasets
| 2 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Q: We build on the state-of-the-art publicly available question answering system by docqa. The system extends BiDAF BIBREF4 with self-attention and performs... | SPTree Tagging CopyR HRL GraphR N-gram Attention
****
| 4 | NIv2 | task460_qasper_answer_generation | fs_opt |
TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
PROBLEM: For the Russian language, with its rich morphology, lemmatizing the training and testing data for ELMo representations yields small... | WN18RR FB15k-237 YAGO3-10
| 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
We defined the reward as being 1 for successfully completing the task, and 0 otherwise. A discount of $0.95$ was used to incentivize t... | training dataset contains 2,815 examples 761 testing examples
| 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Let me give you an example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. ... | TB-Dense MATRES | 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example Input: We make copies of the monolingual model for each language and add additional crosslingual latent variables (CLVs) to couple the monolingual m... | TB-Dense MATRES
| 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
We evaluate the proposed approach o... | we demonstrate that harassment occurred more frequently during the night time than the day time it shows that besides unspecified strangers (not shown in the figure), conductors and drivers are top the list of identified types of harassers, followed by friends and relatives we uncovered that there exist strong correlat... | 0 | NIv2 | task460_qasper_answer_generation | fs_opt |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
We evaluate the proposed approach o... | 0.7033 | 0 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
One example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chin... | politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. | 6 | NIv2 | task460_qasper_answer_generation | fs_opt |
Detailed Instructions: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
See one example below:
Problem: We evaluate the proposed approach on the Chinese social media text summarization task, based on the se... | Using DSC loss improves the F1 score by +0.58 for MRPC and +0.73 for QQP | 4 | NIv2 | task460_qasper_answer_generation | fs_opt |
Part 1. Definition
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Part 2. Example
We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence m... | F-score micro-F macro-F weighted-F | 7 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Let me give you an example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. ... | jointly trained with slots | 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese ... | Solution: size, demographics, areas of research, impact, and correlation of citations with demographic attributes (age and gender) | 5 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
--------
Question: The UNGA speeches dataset, compiled by Baturo et al. UNGAspeeches, contains the text from 7,507 speeches given between 1970-2015 inclusive... | two previous turns
| 7 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
--------
Question: We introduce our proposed diversity, density, and homogeneity metrics with their detailed formulations and key intuitions.
Question: Did... | As the question has integrated previous utterances, the model needs to directly relate previously mentioned concept with the current question. This is helpful for concept carry-over and coreference resolution.
| 7 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
--------
Question: 45 clinically interpretable features per admission were extracted as inputs to the readmission risk classifier. These features can be grou... | Active learning methods has a learning engine (mainly used for training of classification problems) and the selection engine (which chooses samples that need to be relabeled by annotators from unlabeled data). Then, relabeled samples are added to training set for classifier to re-train, thus continuously improving the ... | 7 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
[Q]: In fact, realizing the necessity of large text corpus for Sindhi, we started this research by collecting raw corpus from multiple web resource using we... | Database Construction: we crawled travel information in Beijing from the Web, including Hotel, Attraction, and Restaurant domains (hereafter we name the three domains as HAR domains). Then, we used the metro information of entities in HAR domains to build the metro database. Goal Generation: a multi-domain goal genera... | 5 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese ... | Solution: Logistic regression LSTM End-to-end memory networks Deep projective reader | 5 | NIv2 | task460_qasper_answer_generation | fs_opt |
Teacher: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Teacher: Now, understand the problem? If you are still confused, see the following example:
We evaluate the proposed approach on the Chinese social ... | Bi-LSTM-CRF | 2 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
[EX Q]: The only remaining question is what makes two environments similar enough to infer the existence of a common category. There is, again, a large lite... | Hence WordPiece tokenizer tokenizes noisy words into subwords. However, it ends up breaking them into subwords whose meaning can be very different from the meaning of the original word. Often, this changes the meaning of the sentence completely, therefore leading to substantial dip in the performance.
| 6 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example Input: In Section SECREF16, we first provide more details about the experimental setting that we followed. As explained in Section SECREF3, we use... | The Conversations Gone Awry dataset is labelled as either containing a personal attack from withint (i.e. hostile behavior by one user in the conversation directed towards another) or remaining civil throughout. The Reddit Change My View dataset is labelled with whether or not a coversation eventually had a comment rem... | 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese ... | Solution: rating questions on a scale of 1-5 based on fluency of language used and relevance of the question to the context | 5 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example Input: We use Gaussian Processes as this probabilistic kernelised framework avoids the need for expensive cross-validation for hyperparameter select... | rating questions on a scale of 1-5 based on fluency of language used and relevance of the question to the context
| 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Input: Consider Input: Motivated by this, we introduce resolution mode variables $\Pi = \lbrace \pi _1, \ldots , \pi _n\rbrace $ , where for each mention $j... | Output: average classification accuracy
| 2 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale C... | average classification accuracy | 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale C... | For each cluster, its overall sentiment score is quantified by the mean of the sentiment scores among all tweets | 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example Input: Figure FIGREF9 illustrates the overall architecture of the proposed Attentional Encoder Network (AEN), which mainly consists of an embedding ... | High scores to semantically opposite translations/summaries, Low scores to semantically related translations/summaries and High scores to unintelligible translations/summaries.
| 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
Part 1. Definition
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Part 2. Example
We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence m... | morpheme segmentation BIBREF4 and Byte Pair Encoding (BPE) BIBREF5 Zemberek BIBREF12 | 7 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Let me give you an example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. ... | 9710 passages, with an average of 6.24 sentences per passage, 16.16 words per sentence, and an average length of 86 words | 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
PROBLEM: To evaluate the usefulness of our corpus for SMT purposes, we used it to train an automatic translator with Moses BIBREF8 .
Quest... | language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities) language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words)
| 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
Detailed Instructions: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
See one example below:
Problem: We evaluate the proposed approach on the Chinese social media text summarization task, based on the se... | Clusters of Twitter user ids from accounts of American or German political actors, musicians, media websites or sports club | 4 | NIv2 | task460_qasper_answer_generation | fs_opt |
TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
PROBLEM: In this paper, we first highlight the importance of TV and radio broadcast as a source of data for ASR, and the potential impact it... | A pointer network decodes the answer from a bidirectional LSTM with attention flow layer and self-matching layer, whose inputs come from word and character embeddings of the query and input text fed through a highway layer.
| 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
SVM: We define 3 sets of features to characterize each question. The first is a simple bag-of-words set of features over the question ... | They evaluate F1 score and agent's test performance on their own built interactive datasets (iSQuAD and iNewsQA)
| 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
One example is below.
Q: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Larg... | They identify documents that contain the unigrams 'caused', 'causing', or 'causes' | 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
[Q]: We asked medical doctors experienced in extracting knowledge related to medical entities from texts to annotate the entities described above. Initially... | They identify documents that contain the unigrams 'caused', 'causing', or 'causes'
| 5 | NIv2 | task460_qasper_answer_generation | fs_opt |
Given the task definition, example input & output, solve the new input case.
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summ... | CoNLL 2003 CoNLL 2000 | 1 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example Input: While the performances of a purely content-based model naturally stays stable, the performance of the other systems decrease notably – they p... | Target-1
| 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Let me give you an example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. ... | None | 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Ex Input:
This step consists of generating a query out of the claim and querying a search engine (here, we experiment with Google and Bing) in order to retr... | words extracted from YouTube video comments and descriptions for all YouTube videos shared in the user's timeline
| 1 | NIv2 | task460_qasper_answer_generation | fs_opt |
instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
Given a statement and articles, workers are asked to judge whether the statement can be derived from the articles at three grades: Tru... | no gold standard for automatically evaluating two (or more) dialogue systems when considering the satisfaction of the human and the fluency of the generated dialogue
| 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
PROBLEM: Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares again... | None
| 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
PROBLEM: The second Turkish dataset is the Twitter corpus which is formed of tweets about Turkish mobile network operators. Those tweets are... | Yes
| 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
One example is below.
Q: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Larg... | train the parser on six other languages in the Google universal dependency treebanks version 2.0 (de, en, es, fr, it, pt, sv, excluding whichever is the target language), and we use gold coarse POS tags | 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
PROBLEM: Given a statement and articles, workers are asked to judge whether the statement can be derived from the articles at three grades: ... | the annotation machinery of BIBREF5
| 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
Teacher: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Teacher: Now, understand the problem? If you are still confused, see the following example:
We evaluate the proposed approach on the Chinese social ... | we sort the speech segments by length we take segments in pairs, zero-padding the shorter segment so both have the same length These pairs are then mixed together | 2 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
One example is below.
Q: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Larg... | Hasty Student Impatient Reader BiDAF BiDAF w/ static memory | 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
Given the task definition, example input & output, solve the new input case.
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summ... | ability to accurately classify texts even when the amount of prior knowledge for different classes is unbalanced, and when the class distribution of the dataset is unbalanced | 1 | NIv2 | task460_qasper_answer_generation | fs_opt |
TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
PROBLEM: We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from poste... | ability to accurately classify texts even when the amount of prior knowledge for different classes is unbalanced, and when the class distribution of the dataset is unbalanced
| 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale C... | Lexicon Embedding Layer Context Embedding Layer Coarse Memory Layer Refined Memory Layer Answer Span Prediction Layer | 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
Given the task definition, example input & output, solve the new input case.
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summ... | it is less expensive and quantifies interpretability using continuous values rather than binary evaluations | 1 | NIv2 | task460_qasper_answer_generation | fs_opt |
Teacher: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Teacher: Now, understand the problem? If you are still confused, see the following example:
We evaluate the proposed approach on the Chinese social ... | fine-tuned the GPT-2 medium model BIBREF51 on our collected headlines and then used it to measure the perplexity (PPL) on the generated outputs | 2 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese ... | Solution: The reciprocal of the variance of the attention distribution | 5 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Input: Consider Input: Three different datasets have been used to train our models: the Toronto book corpus, Wikipedia sentences and tweets. Our Sent2Vec m... | Output: File IO Standard IO Telegram
| 2 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Input: Consider Input: KGR10, also known as plWordNet Corpus 10.0 (PLWNC 10.0), is the result of the work on the toolchain to automatic acquisition and extr... | Output: 77 genres
| 2 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
One example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chin... | No | 6 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Experimental Setup
Question: what english datasets were used?
Answer with content missing: (Data section) Penn Treebank (PTB)
We conduct experiments on o... | PER, LOC, ORG, MISC
| 0 | NIv2 | task460_qasper_answer_generation | fs_opt |
instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-relat... | answer questions by obtaining information from KB tuples
| 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Let me give you an example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. ... | answer questions by obtaining information from KB tuples | 8 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
One example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chin... | the personalized bAbI dialog dataset | 6 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example Input: As we can see that, all variants of our CRU model could give substantial improvements over the traditional GRU model, where a maximum gain of... | Yes
| 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
One example is below.
Q: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Larg... | INLINEFORM0 scores | 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale C... | Penn Treebank Text8 WSJ10 | 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Q: In case of polysemous words, only the first word sense (usually the most common) is taken into account.
Question: How do they handle polysemous words i... | user holds or points weapons, is seen in a group fashion which displays a gangster culture, or is showing off graffiti, hand signs, tattoos and bulk cash
****
| 4 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Q: Despite this, we found that the training of our embeddings was not considerably slower than the training of order-2 equivalents such as SGNS. Explicitly,... | Hindi, English and German (German task won)
****
| 4 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
One example is below.
Q: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Larg... | The coefficients are projected back to the dummy variable space. | 9 | NIv2 | task460_qasper_answer_generation | fs_opt |
Part 1. Definition
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Part 2. Example
We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence m... | Twitter definition of hateful conduct | 7 | NIv2 | task460_qasper_answer_generation | fs_opt |
Part 1. Definition
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Part 2. Example
We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence m... | AI2 BIBREF2 CC BIBREF19 IL BIBREF4 MAWPS BIBREF20 | 7 | NIv2 | task460_qasper_answer_generation | fs_opt |
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example Input: Validated transcripts were sent to professional translators. In order to control the quality of the professional translations, we applied var... | AI2 BIBREF2 CC BIBREF19 IL BIBREF4 MAWPS BIBREF20
| 3 | NIv2 | task460_qasper_answer_generation | fs_opt |
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