Question Answering Infused Pre-training of General-Purpose Contextualized Representations

@article{Jia2022QuestionAI,
  title={Question Answering Infused Pre-training of General-Purpose Contextualized Representations},
  author={Robin Jia and Mike Lewis and Luke Zettlemoyer},
  journal={ArXiv},
  year={2022},
  volume={abs/2106.08190}
}
We propose a pre-training objective based on question answering (QA) for learning general-purpose contextual representations, motivated by the intuition that the representation of a phrase in a passage should encode all questions that the phrase can answer in context. To this end, we train a bi-encoder QA model, which independently encodes passages and questions, to match the predictions of a more accurate cross-encoder model on 80 million synthesized QA pairs. By encoding QA-relevant… 

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References

SHOWING 1-10 OF 75 REFERENCES

Learning Dense Representations of Phrases at Scale

TLDR
This work shows for the first time that it can learn dense representations of phrases alone that achieve much stronger performance in open-domain QA and proposes a query-side fine-tuning strategy, which can support transfer learning and reduce the discrepancy between training and inference.

Making Pre-trained Language Models Better Few-shot Learners

TLDR
The LM-BFF approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning.

BERTScore: Evaluating Text Generation with BERT

TLDR
This work proposes BERTScore, an automatic evaluation metric for text generation that correlates better with human judgments and provides stronger model selection performance than existing metrics.

PAWS: Paraphrase Adversaries from Word Scrambling

TLDR
PAWS (Paraphrase Adversaries from Word Scrambling), a new dataset with 108,463 well-formed paraphrase and non-paraphrase pairs with high lexical overlap, is introduced, providing an effective instrument for driving further progress on models that better exploit structure, context, and pairwise comparisons.

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

TLDR
A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.

Automatically Constructing a Corpus of Sentential Paraphrases

TLDR
The creation of the recently-released Microsoft Research Paraphrase Corpus, which contains 5801 sentence pairs, each hand-labeled with a binary judgment as to whether the pair constitutes a paraphrase, is described.

First quora dataset release: Question pairs

  • https://www.quora.com/q/quoradata/ First-Quora-Dataset-Release-Question-Pairs.
  • 2017

RoBERTa: A Robustly Optimized BERT Pretraining Approach

TLDR
It is found that BERT was significantly undertrained, and can match or exceed the performance of every model published after it, and the best model achieves state-of-the-art results on GLUE, RACE and SQuAD.

Synthetic QA Corpora Generation with Roundtrip Consistency

TLDR
A novel method of generating synthetic question answering corpora is introduced by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency, establishing a new state-of-the-art on SQuAD2 and NQ.

The Curious Case of Neural Text Degeneration

TLDR
By sampling text from the dynamic nucleus of the probability distribution, which allows for diversity while effectively truncating the less reliable tail of the distribution, the resulting text better demonstrates the quality of human text, yielding enhanced diversity without sacrificing fluency and coherence.
...