Papers with MultiRC
What to Learn, and How: Toward Effective Learning from Rationales (2022.findings-acl)
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| Challenge: | Increasing interest in learning from rationales has led to the use of human-annotated explanations to inject useful inductive biases into models. |
| Approach: | They propose several novel loss functions and learning strategies to exploit human rationales to augment model prediction accuracy. |
| Outcome: | The proposed learning strategies improve on three datasets with human rationales and show that they are more efficient than baselines. |
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text (P19-1)
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| 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. |
Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering (D19-1)
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| Challenge: | Arras et al., 2017) suggest an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) . |
| Approach: | They propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering that maximizes the relevance of the selected sentences, minimizes overlap between selected facts, and maximizes coverage of both question and answer. |
| Outcome: | The proposed strategy improves state-of-the-art supervised QA model on two multi-hop QA datasets: AI2’s Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC). |
Improving Machine Reading Comprehension with General Reading Strategies (N19-1)
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| Challenge: | Recent studies have shown that reading strategies improve comprehension levels for readers lacking adequate prior knowledge. |
| Approach: | They propose three general strategies to improve machine reading comprehension (MRC) by fine-tuning a pre-trained model with strategies and a target task. |
| Outcome: | The proposed models improve non-extractive machine reading comprehension (MRC) on the largest general domain multiple-choice dataset RACE. |
Repurposing Entailment for Multi-Hop Question Answering Tasks (N19-1)
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| Challenge: | Existing approaches to use entailment models for question answering are limited . large scale datasets are typically framed at a sentence level, whereas question answering requires verifying whether multiple sentences, taken together as a premise, entitle a hypothesis. |
| Approach: | They propose a general architecture that can use entailment models for multi-hop QA tasks. |
| Outcome: | The proposed model outperforms QA models trained on target datasets and the OpenAI transformer models. |
If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering (2021.naacl-main)
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| Challenge: | et al. : evidence retrieval is highly dependent on partial, incorrect or no supporting knowledge. |
| Approach: | They propose a method that retrieves and reranks evidence facts jointly . they propose to account for links between sentences and coverage with the given query . |
| Outcome: | The proposed approach achieves state-of-the-art evidence retrieval performance on two multi-hop question answering datasets. |
Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering (2020.acl-main)
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| Challenge: | Evidence retrieval is a critical stage of question answering (QA) . Several multi-hop QA datasets have been proposed recently . |
| Approach: | They propose an unsupervised method that uses only GloVe embeddings to soft-align questions with justification sentences and an iterative process that reformulates queries focusing on terms that are not covered by existing justifications. |
| Outcome: | The proposed method outperforms all previous methods on the evidence selection task on two datasets: MultiRC and QASC. |
Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian (2020.coling-main)
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| Challenge: | MRC in other languages, including Russian, has not been well-addressed due to the lack of high-quality and large-scale datasets. |
| Approach: | They propose two Russian machine reading comprehension datasets that require reasoning over multiple sentences and commonsense knowledge to infer the answer. |
| Outcome: | The proposed datasets are more complex than the original ones for Russian . the results show that the proposed models are challenging for advanced models . |