EEE-QA: Exploring Effective and Efficient Question-Answer Representations (2024.lrec-main)
Copied to clipboard
| Challenge: | Current approaches to question answering rely on pre-trained language models like RoBERTa. |
| Approach: | They propose a pooling approach that embeds all answer candidates with the question . they also propose enabling cross-reference between answer choices . |
| Outcome: | The proposed methods improve throughput and memory efficiency with little sacrifice in performance. |
Similar Papers
QuASE: Question-Answer Driven Sentence Encoding (2020.acl-main)
Copied to clipboard
| Challenge: | Question-answering (QA) data often encodes essential information in many facets . a growing interest of QA has led to many large-scale QA datasets available to the community . |
| Approach: | They propose a question-answer driven sentence encoding framework to learn representations from QA data. |
| Outcome: | The proposed framework learns representations from QA data, using BERT or other state-of-the-art contextual language models. |
Question Answering Infused Pre-training of General-Purpose Contextualized Representations (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing pretraining objectives for question answering (QA) are not optimized for being immediately useful without fine-tuning. |
| Approach: | They propose a pre-training objective based on question answering (QA) that is based more directly on context. |
| Outcome: | The proposed model matches predictions of a more accurate cross-encoder model on 80 million synthesized QA pairs and achieves large improvements over previous state-of-the-art models on paraphrase detection and fewshot named entity recognition. |
Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for open-domain question-answering use an open book approach . a recent alternative is to retrieve from a collection of previously-generated question-annwer pairs . |
| Approach: | They propose a new QA system that augments a text-to-text model with a large memory of question-answer pairs and a task for the latent step of question retrieval. |
| Outcome: | The proposed system outperforms closed-book QA and can answer multi-hop questions. |
Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA? (2021.acl-long)
Copied to clipboard
| Challenge: | Existing work is limited in using small benchmarks with high test-train overlaps. |
| Approach: | They construct a dataset of closed-book QA using SQuAD and investigate the performance of BART. |
| Outcome: | Experiments show that pre-trained language models can achieve high performance on closed-book QA tasks. |
Dense Passage Retrieval for Open-Domain Question Answering (2020.emnlp-main)
Copied to clipboard
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih
| Challenge: | Open-domain question answering relies on efficient passage retrieval to select candidate contexts. |
| Approach: | They propose a dual-encoder framework that can be implemented to retrieve passages from a small number of questions and passages. |
| Outcome: | The proposed system outperforms a strong Lucene-BM25 system in top-20 passage retrieval accuracy on multiple open-domain QA benchmarks. |
Towards more equitable question answering systems: How much more data do you need? (2021.acl-short)
Copied to clipboard
| Challenge: | Question answering datasets in English are relatively new, but lack of linguistic diversity in the field is a challenge. |
| Approach: | They propose to use translation and cross-lingual transfer to produce QA systems in multiple languages to improve their performance. |
| Outcome: | The proposed approaches take advantage of existing resources to produce QA systems in multiple languages. |
PEDANTS: Cheap but Effective and Interpretable Answer Equivalence (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Current short-form QA evaluations lack diverse styles of evaluation data and rely on expensive and slow LLMs. |
| Approach: | They propose a rubric for machine QA that is more stable than an exact match and neural methods. |
| Outcome: | The proposed evaluations improve on the existing short-form QA evaluations using the Trivia community. |
Simple and Effective Semi-Supervised Question Answering (N18-2)
Copied to clipboard
| Challenge: | Existing deep learning systems for extractive Question Answering are limited and expensive to construct. |
| Approach: | They propose a semi-supervised QA system where end user specifies a set of documents and only a few labelled examples. |
| Outcome: | The proposed system achieves 50% F1 score on SQuAD and TriviaQA with very little labeled data. |
Harvesting and Refining Question-Answer Pairs for Unsupervised QA (2020.acl-main)
Copied to clipboard
| Challenge: | Recent research attempts to extend unsupervised question answering to settings with few or no labeled data available. |
| Approach: | They propose two approaches to improve unsupervised question answering . first, they harvest lexically and syntactically divergent Wikipedia questions to automatically construct a corpus of question-answer pairs . second, they take advantage of the QA model to extract more appropriate answers . |
| Outcome: | The proposed approach outperforms previous unsupervised approaches by a large margin and is competitive with early supervised models. |
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Among recent NLP research, multi-document processing is gaining increasing attention due to the need to handle and process an increasing amount of textual data and available documents online. |
| Approach: | They propose to pre-train a generic multi-document model from a cross-document question answering pre-training objective by generating salient sentences from one document and challenging it to recover the sentence from which it was generated. |
| Outcome: | The proposed model outperforms zero-shot GPT-3.5 and GPT-4 in multiple document tasks and generates the correct answer and the salient sentence from a salient document. |