| Challenge: | Existing models for text-based multiple choice question answering are based on a text. |
| Approach: | They propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. |
| Outcome: | The proposed model outperforms several baseline models on the SciQ and TQA datasets. |
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A Neural Question Answering Model Based on Semi-Structured Tables (C18-1)
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| Challenge: | Existing question answering systems rely on raw text and structured knowledge graphs. |
| Approach: | They build an end-to-end system to answer multiple choice questions with semi-structured tables as its knowledge. |
| Outcome: | The proposed system improves on the state-of-the-art question answering system with tabMCQ dataset. |
RankQA: Neural Question Answering with Answer Re-Ranking (P19-1)
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| Challenge: | RankQA extends the conventional two-stage process in neural question answering . RankQ achieves state-of-the-art performance on 3 out of 4 benchmark datasets . |
| Approach: | They propose to extend the conventional two-stage process in neural QA with a third stage that performs an additional answer re-ranking. |
| Outcome: | RankQA outperforms more complex question answering systems by a significant margin on 3 out of 4 benchmark datasets. |
Training a Ranking Function for Open-Domain Question Answering (N18-4)
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| Challenge: | Recent advances in machine reading have inspired researchers to combine Information Retrieval with machine reading to tackle open-domain QA. |
| Approach: | They propose two neural network rankers that assign scores to different passages based on their likelihood of containing the answer to a given question. |
| Outcome: | The proposed models achieve human level performance in open-domain QA compared to reading comprehension-style QA because it is difficult to retrieve the pieces of paragraphs that contain the answer to the question. |
Answering questions by learning to rank - Learning to rank by answering questions (D19-1)
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| Challenge: | Existing approaches to answer multiple-choice questions with no supporting documents are poor performance. |
| Approach: | They propose a method which can be used to semantically rank documents extracted from Wikipedia . they propose 'semantic ranking' method that latently learns to rank documents by their importance . |
| Outcome: | The proposed model achieves state-of-the-art accuracy on two datasets: ARC Easy and Challenge. |
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
| Outcome: | The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis. |
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering (C18-1)
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| Challenge: | Sentence pair modeling is a fundamental technique underlying many NLP tasks. |
| Approach: | They analyze several neural network designs for sentence pair modeling and compare their performance extensively across eight datasets. |
| Outcome: | The proposed models perform well across eight datasets including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks. |
Question Answering by Reasoning Across Documents with Graph Convolutional Networks (N19-1)
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| Challenge: | Recent research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. |
| Approach: | They propose a neural model which integrates and reasons relying on information spread within documents and across multiple documents. |
| Outcome: | The proposed model achieves state-of-the-art on a multi-document question answering dataset, WikiHop. |
Simple and Effective Multi-Paragraph Reading Comprehension (P18-1)
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| Challenge: | Existing question answering models cannot scale beyond short paragraphs, so adapting a model to document-level input is difficult. |
| Approach: | They propose a method of adapting neural paragraph-level question answering models to document input. |
| Outcome: | The proposed method achieves state-of-the-art on TriviaQA and SQuAD and a 10 point gain on SQuADA. |
Adaptive Document Retrieval for Deep Question Answering (D18-1)
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| Challenge: | Existing methods for deep question answering do not understand the exact interplay between document retrieval and machine comprehension. |
| Approach: | They propose an adaptive document retrieval model that learns the optimal document number, conditional on the size of the corpus and the query. |
| Outcome: | The proposed model outperforms state-of-the-art methods on multiple benchmark datasets and in the context of corpora with variable sizes. |
Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks (2020.lrec-1)
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| Challenge: | Existing question-answering models do not require reasoning across sentences in the given context (passage). |
| Approach: | They propose a graph neural network that propagates information over sentences to understand information that cannot be inferred when considering sentences in isolation. |
| Outcome: | The proposed approach obtains the best performance compared to the widely used answer-selection models that do not consider the intersentential relationship. |