M3: A Multi-View Fusion and Multi-Decoding Network for Multi-Document Reading Comprehension (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for multi-document reading comprehension cannot make full of the advantages of both approaches. |
| Approach: | They propose a multi-view fusion and multi-decoding method that integrates multiple documents for answering questions. |
| Outcome: | The proposed method improves on two mainstream multi-document reading comprehension datasets. |
Similar Papers
Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering (P18-1)
Copied to clipboard
| 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. |
Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to answer reading comprehension tasks are inefficient since the input is re-encoded within each module. |
| Approach: | They propose a unified question answering model that combines context retrieving, reading comprehension, and answer reranking to predict the final answer. |
| Outcome: | The proposed model outperforms the baseline model and achieves state-of-the-art results on two versions of TriviaQA and two variants of SQuAD. |
Multi-Granularity Guided Fusion-in-Decoder (2024.findings-naacl)
Copied to clipboard
| Challenge: | Open-domain question answering requires deriving factual responses without explicit evidence . recent approaches combine retrieval of relevant information with response generation . |
| Approach: | They propose a model that concatenates multiple contexts in the decoding phase . they propose MGFiD, which harmonizes passage re-ranking with sentence classification . |
| Outcome: | The proposed model outperforms existing models on Natural Questions and TriviaQA datasets . it aggregates evident sentences into an anchor vector that instructs the decoder . |
Multi-doc Hybrid Summarization via Salient Representation Learning (2023.acl-industry)
Copied to clipboard
| Challenge: | Multi-document summarization is gaining more and more attention . extractive multi-doc approaches intend to directly extract key facts from multiple sources . |
| Approach: | They propose a multi-document hybrid summarization approach that generates a human-readable summary and extracts corresponding key evidences based on multi-doc inputs. |
| Outcome: | The proposed method generates a human-readable summary and extracts key evidences based on multi-doc inputs. |
Multi-style Generative Reading Comprehension (P19-1)
Copied to clipboard
Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, Junji Tomita
| Challenge: | Current studies on generative reading comprehension (RC) focus on extracting an answer span from textual evidence and natural language generation (NLG). |
| Approach: | They propose a multi-style abstractive summarization model for question answering called Masque. |
| Outcome: | The proposed model achieves state-of-the-art performance on the Q&A and Q& A + NLG tasks of MS MARCO and NarrativeQA. |
Multi-view and Cross-view Brain Decoding (2022.coling-1)
Copied to clipboard
| Challenge: | a recent study has shown that brain decoding models can decode concepts from single view . a multi-view decoder can take brain recordings for any view as input and predict the concept . |
| Approach: | They propose to build a multi-view decoder that can take brain recordings for any view as input and predict the concept. |
| Outcome: | The proposed systems can decode concepts from brain recordings from any view . the proposed systems have 0.68 pairwise accuracy across view pairs and 0.8 average pairwise precision across tasks. |
Cross-Task Knowledge Transfer for Query-Based Text Summarization (D19-58)
Copied to clipboard
| Challenge: | Existing methods for summarization data corpora are limited to extractive and abstractive summarizing. |
| Approach: | They propose to use machine reading comprehension (MRC) and query-based text summarization to produce extractive and abstractive summaries from pre-trained MRC and MT models. |
| Outcome: | The proposed model outperforms existing methods on CNN/Daily Mail and Debatepedia datasets and can be used as a baseline for future systems. |
Hierarchical Transformers for Multi-Document Summarization (P19-1)
Copied to clipboard
| Challenge: | Existing models for multidocument summarization have been developed that can process multiple documents in a hierarchical manner. |
| Approach: | They propose a neural summarization model which can process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. |
| Outcome: | The proposed model improves on the WikiSum dataset and can process multiple documents in a hierarchical manner. |
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text (P19-1)
Copied to clipboard
| Challenge: | Experimental results on 3 popular datasets demonstrate the effectiveness of our approach. |
| Approach: | They propose a network to solve the inference problem by decomposing text into a series of attention-based reasoning steps. |
| Outcome: | The proposed network can be used to understand the meanings of given text to answer questions. |
Parallel Context-of-Experts Decoding for Retrieval Augmented Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Retrieval Augmented Generation relies on concatenating documents into a long context prompt, causing prefill bottlenecks. |
| Approach: | They propose a training-free framework that shifts evidence aggregation from attention to decoding . they treat retrieved documents as isolated "experts", synchronizing their predictions via a retrieval-aware extension of context-awful decoding. |
| Outcome: | The proposed framework shifts evidence aggregation from attention to decoding . it treats retrieved documents as isolated experts, synchronizing their predictions . |