Question Answering is the task of answering questions (typically reading comprehension questions), but abstaining when presented with a question that cannot be answered based on the provided context ( Image credit: SQuAD )
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To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
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Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not.
Ranked #6 on Question Answering on Quora Question Pairs
The main experimental result in this paper is that a single Neural Programmer model achieves 34. 2% accuracy using only 10, 000 examples with weak supervision.
We introduce "talking-heads attention" - a variation on multi-head attention which includes linearprojections across the attention-heads dimension, immediately before and after the softmax operation. While inserting only a small number of additional parameters and a moderate amount of additionalcomputation, talking-heads attention leads to better perplexities on masked language modeling tasks, aswell as better quality when transfer-learning to language comprehension and question answering tasks.
Community Question-Answering websites, such as StackOverflow and Quora, expect users to follow specific guidelines in order to maintain content quality.
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Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
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We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Ranked #1 on Question Answering on CoQA
COMMON SENSE REASONING CONVERSATIONAL RESPONSE SELECTION CROSS-LINGUAL NATURAL LANGUAGE INFERENCE NAMED ENTITY RECOGNITION NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING SENTENCE CLASSIFICATION SENTIMENT ANALYSIS
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
Ranked #1 on Question Answering on SQuAD1.1 dev
COMMON SENSE REASONING COREFERENCE RESOLUTION LINGUISTIC ACCEPTABILITY NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING READING COMPREHENSION SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS WORD SENSE DISAMBIGUATION
Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks.
Ranked #6 on Question Answering on Natural Questions (short)