A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning (D19-1)
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| Challenge: | Existing models for reading comprehension and question answering do not support discrete reasoning abilities. |
| Approach: | They propose a reading comprehension model that uses a multi-type answer predictor and a multiple-span extraction method to produce one or multiple text spans. |
| Outcome: | The proposed model achieves 79.9 F1 on the DROP hidden test set, creating new state-of-the-art results. |
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A Simple and Effective Model for Answering Multi-span Questions (2020.emnlp-main)
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| Challenge: | Existing models for reading comprehension restrict output space to a set of single contiguous spans . multi-span questions are problematic because they require multiple inputs - a task that requires a sequence tagging problem . |
| Approach: | They propose a simple architecture for answering multi-span questions by casting the task as a sequence tagging problem. |
| Outcome: | The proposed model significantly improves performance on span extraction questions from DROP and Quoref by 9.9 and 5.5 EM points respectively. |
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension (D18-1)
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| Challenge: | Recent deep-learning based models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span. |
| Approach: | They propose a novel context zoom-in network (ConZNet) that can skip through irrelevant parts of a document and generate an answer using only the relevant regions of text. |
| Outcome: | The proposed architecture outperforms state-of-the-art results by 12.62% (ROUGE-L) relative improvement on the recently proposed and challenging RC dataset ‘NarrativeQA’. |
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification (P18-1)
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| Challenge: | Recent years have seen rapid growth in the MRC community . MRC is believed to be a crucial step in building a general intelligent agent . |
| Approach: | They propose an end-to-end neural model that enables multiple passages to verify each other based on their content representations. |
| Outcome: | The proposed model outperforms the baseline on the English MS-MARCO dataset and the Chinese DuReader dataset, and achieves state-of-the-art performance on both datasets. |
MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension (2020.aacl-main)
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| Challenge: | Existing methods to predict the start and end positions of answer spans generate two probability vectors. |
| Approach: | They propose a method that extends the probability vector to a probability matrix. |
| Outcome: | The proposed method improves on SQuAD 1.1 and three other question answering benchmarks. |
Stochastic Answer Networks for Machine Reading Comprehension (P18-1)
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| Challenge: | Several recent MRC models employ multi-step reasoning . we show that the use of a stochastic prediction dropout improves robustness . |
| Approach: | They propose a stochastic answer network that simulates multi-step reasoning in machine reading comprehension. |
| Outcome: | The proposed model improves robustness and results competitive with state-of-the-art models on the Stanford Question Answering Dataset and Microsoft MAchine Reading COmprehension Dataset. |
MultiSpanQA: A Dataset for Multi-Span Question Answering (2022.naacl-main)
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| Challenge: | Existing reading comprehension datasets focus on single-span answers, but multi-spread questions are less studied. |
| Approach: | They propose a new reading comprehension dataset that focuses on multi-span questions . they introduce new metrics for the purposes of multi--spontaneous question answering evaluation . |
| Outcome: | The proposed model beats baselines and achieves state-of-the-art on the existing dataset. |
Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension (P19-1)
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| Challenge: | Multi-passage reading comprehension requires the ability to combine cross-passages information and reason over multiple passages to infer the answer. |
| Approach: | They propose a Dynamic Self-attention Network (DynSAN) which processes cross-passage information at token-level and meanwhile avoids substantial computational costs. |
| Outcome: | The proposed model achieves state-of-the-art performance on the SearchQA, Quasar-T and WikiHop datasets and further ablation validates the effectiveness of its components. |
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension (2024.emnlp-main)
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| Challenge: | Extractive Machine Reading Comprehension (MRC) is a challenging field in the field of Natural Language Processing. |
| Approach: | They propose a Question-Attended Span Extraction module to address the limitations of generative approaches for extractive machine reading comprehension (MRC) . module significantly enhances performance of pre-trained generative language models, enabling them to surpass the extractive capabilities of advanced Large Language Models (LLMs) |
| Outcome: | The QASE module surpasses state-of-the-art models in few-shot settings. |
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension (2020.acl-main)
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| Challenge: | Existing approaches to machine reading comprehension (MRC) on long texts typically chunk text into equally-spaced segments without considering information from other segments. |
| Approach: | They propose to let a model learn to chunk in a more flexible way via reinforcement learning. |
| Outcome: | The proposed model extracts a text span from document and query as answer . previous models can only take a fixed-length (e.g., 512) text as input . |
Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering (P18-1)
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| Challenge: | Existing approaches to read comprehension style question answering are limited by the volume of annotated datasets. |
| Approach: | They propose a hierarchical attention network for reading comprehension style question answering . they first encode the question and paragraph with fine-grained language embeddings . then propose fusion approach to fuse information from both global and attended representations based on the hierarchic attention network . |
| Outcome: | The proposed method achieves state-of-the-art on the SQuAD and TriviaQA Wiki leaderboards and two adversarial SQu AD datasets. |