1088 papers with code • 64 benchmarks • 249 datasets
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 )
To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
Ranked #1 on Question Answering on Natural Questions (F1 (Long) metric)
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 #7 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.
Community Question-Answering websites, such as StackOverflow and Quora, expect users to follow specific guidelines in order to maintain content quality.
Ranked #1 on Question Quality Assessment on CrowdSource QA
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Ranked #1 on Question Answering on CoQA
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.
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
Ranked #1 on Natural Language Inference on QNLI
In this paper, we illustrate how to fine-tune the entire Retrieval Augment Generation (RAG) architecture in an end-to-end manner.
Ranked #1 on Question Answering on SQuAD
The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks.
Ranked #2 on Reading Comprehension on MuSeRC