Proceedings of the 2nd Workshop on Machine Reading for Question Answering
MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension (D19-58)
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| Challenge: | MRQA datasets have been used to benchmark progress in general-purpose language understanding. |
| Approach: | They propose to combine 18 question answering datasets into one shared task to evaluate their generalization capabilities. |
| Outcome: | The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than baseline based on BERT. |
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension (D19-58)
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| Challenge: | Experimental results show that unified model outperforms other models that treat encoding and matching separately. |
| Approach: | They evaluate a unified model with Transformer layers for machine reading comprehension . they find that the model learns different modeling strategies compared with previous models . |
| Outcome: | The unified model outperforms models with Transformer layers on the machine reading comprehension task. |
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations (D19-58)
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Frederic Bechet, Cindy Aloui, Delphine Charlet, Geraldine Damnati, Johannes Heinecke, Alexis Nasr, Frederic Herledan
| Challenge: | Recent large corpora of triplets have opened the door to supervised machine learning approaches for Question-Answering. |
| Approach: | They propose to generate questions from the semantic Frame analysis of large corpora using a CALOR-QUEST resource in French and use it to improve machine reading comprehension. |
| Outcome: | The proposed method generates questions from the semantic Frame analysis of large corpora and then tests them on the CALOR-QUEST resource in French. |
Improving Question Answering with External Knowledge (D19-58)
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| Challenge: | ARC-Easy, ARC Challenge, and OpenBookQA use Wikipedia to augment training data . performance degrades when additional instances exhibit higher difficulty than original training data. |
| Approach: | They propose two methods for exploiting external knowledge for QA in science . they enrich the original corpus with relevant text snippets from an open-domain resource . the second method simply increases the amount of training data by appending additional in-domain instances. |
| Outcome: | The proposed methods achieve gains in accuracy of 8.1%, 13.0%, and 12.8% on science QA tasks. |
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension (D19-58)
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| Challenge: | Existing question reformulation models are based on supervised question labels without considering feedback information from answers. |
| Approach: | They propose a question reformulation model that integrates conversational history information with reinforcement learning. |
| Outcome: | The proposed model is more effective in conversational machine comprehension with reinforcement learning. |
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering (D19-58)
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| Challenge: | Existing work on open-domain multi-hop question answering relies on off-the-shelf information retrieval techniques to retrieve answer passages. |
| Approach: | They propose a new subproblem for open-domain multi-hop question answering . they aim to recognize the anchor from a set of start passages with a reading comprehension model . |
| Outcome: | The proposed method significantly improves the baseline method on the open-domain hotpotQA benchmark. |
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior (D19-58)
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| Challenge: | Recent studies indicate that the current machine reading comprehension systems suffer from weak robustness against adversarial samples. |
| Approach: | They propose to take sentence syntax as the leverage in the answer predicting process and exploit the syntactic elements of a question to improve the generalization and robustness of MRC models. |
| Outcome: | The proposed method improves generalization and robustness against adversarial samples, with performance well-maintained. |
Reasoning Over Paragraph Effects in Situations (D19-58)
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| Challenge: | a key component of reading a passage is the ability to apply knowledge gained from the passage to a new situation. |
| Approach: | They propose a benchmark for reading comprehension targeting Reasoning Over Paragraph Effects in Situations. |
| Outcome: | The proposed model performs slightly better than randomly guessing an answer of the correct type, but is below the human performance of 89.0%. |
Towards Answer-unaware Conversational Question Generation (D19-58)
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| Challenge: | Existing frameworks for conversational question generation are answeraware, but are not able to generate corresponding answers . a number of question generation methods are developed for text-based question answering . |
| Approach: | They propose a framework for conversational question generation that is unaware of the corresponding answers. |
| Outcome: | The proposed framework is effective but answeraware, the authors show . it improves quality of generated questions if question foci and question patterns are identified . |
Cross-Task Knowledge Transfer for Query-Based Text Summarization (D19-58)
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| 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. |
Book QA: Stories of Challenges and Opportunities (D19-58)
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| Challenge: | Existing approaches to answer questions based on the full text of books are limited by their unique characteristics. |
| Approach: | They propose a system for answering questions based on the full text of books . they use a memory network to reason and predict an answer, and a novel question generator to improve generalization. |
| Outcome: | The proposed system improves on the recently published NarrativeQA corpus on Who questions . it shows that the proposed system is highly challenging and needs more research . |
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension (D19-58)
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| Challenge: | Existing machine comprehension models focus on a single-turn setting and do not account for previous reasoning processes. |
| Approach: | They propose to explicitly model the information gain through the dialogue reasoning . they propose to apply the proposed mechanism to other machine comprehension models . |
| Outcome: | The proposed model achieves state-of-the-art performance in a conversational QA dataset QuAC and a sequential instruction understanding dataset SCONE. |
Do Multi-hop Readers Dream of Reasoning Chains? (D19-58)
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| Challenge: | Existing models for multihop reasoning are limited in their performance . multi-hop reasoning requires the ability to gather information from multiple passages . |
| Approach: | They propose a method that provides the full reasoning chain of multiple passages instead of just one final passage where the answer appears. |
| Outcome: | The proposed model improves on existing models by providing the full reasoning chain of multiple passages instead of just one final passage where the answer appears. |
Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis (D19-58)
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| Challenge: | a lack of gold datasets and knowledge about PAS analysis makes it difficult to create accurate PAS analyses. |
| Approach: | They construct a Japanese blog-QA dataset and a reading comprehension QA dataset using crowdsourcing. |
| Outcome: | The proposed method is most effective, pre-training model to acquire domain knowledge and fine-tuning model based on PAS-QA dataset. |
On Making Reading Comprehension More Comprehensive (D19-58)
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| Challenge: | Getting machines to "understand" text is a vast and long-standing problem, made more challenging by the fact that it is not even clear what it means to understand text. |
| Approach: | They propose a question-based approach to machine reading comprehension that uses a natural language question to test a system's comprehension of a passage of text. |
| Outcome: | The proposed questions have surface cues or other biases that allow a model to shortcut the intended reasoning process. |
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering (D19-58)
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Rajarshi Das, Ameya Godbole, Dilip Kavarthapu, Zhiyu Gong, Abhishek Singhal, Mo Yu, Xiaoxiao Guo, Tian Gao, Hamed Zamani, Manzil Zaheer, Andrew McCallum
| Challenge: | Multi-hop question answering (QA) requires an information retrieval system that can find multiple supporting evidence needed to answer the question. |
| Approach: | They propose a technique that uses information of entities present in the initial retrieved evidence to learn to ‘hop’ onto other relevant evidence. |
| Outcome: | The proposed method boosts retrieval performance on a multi-hop question answering dataset with 5 million Wikipedia paragraphs and a model without training increases its performance by 10.59 F1. |
Evaluating Question Answering Evaluation (D19-58)
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| Challenge: | Existing n-gram based QA metrics have a number of drawbacks and are not suitable for all extractive tasks. |
| Approach: | They propose to use BERTScore to evaluate translation for question answering (QA) they also explore whether existing n-gram based metrics are suitable for generative QA . |
| Outcome: | The proposed BERTScore metric fails to provide stronger correlation with human judgements . |
Bend but Don’t Break? Multi-Challenge Stress Test for QA Models (D19-58)
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| Challenge: | a gap remains in reasoning ability compared to a human, and performance tends to degrade when models are exposed to less-constrained tasks. |
| Approach: | They conduct extensive qualitative and quantitative analyses on the results of four models across four datasets . they relate common errors to model capabilities and discuss a way forward . |
| Outcome: | The proposed model performance is based on the results of four models across four datasets. |
ReQA: An Evaluation for End-to-End Answer Retrieval Models (D19-58)
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| Challenge: | Popular QA benchmarks like SQuAD have driven progress on identifying answer spans within a specific passage . retrieving relevant answers from a huge corpus of documents is still a challenging problem . |
| Approach: | They propose a benchmark for evaluating large-scale sentence-level answer retrieval models . they establish baselines using both neural encoding models and classical retrieval techniques . |
| Outcome: | The proposed model outperforms human models on identifying answer spans within a specific passage . the proposed model is scalable and can bypass the typical document retrieval step . |
Comprehensive Multi-Dataset Evaluation of Reading Comprehension (D19-58)
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| Challenge: | Recent research aims to facilitate training and evaluation on several reading comprehension datasets at the same time. |
| Approach: | They propose an evaluation server that reports performance on seven diverse reading comprehension datasets and includes synthetic augmentations to test models' ability to handle out-of-domain questions. |
| Outcome: | The evaluation server performs on seven reading comprehension datasets, and collects and includes synthetic augmentations for these datasets to test models' ability to handle out-of-domain questions. |
A Recurrent BERT-based Model for Question Generation (D19-58)
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| Challenge: | Existing QG models rely on recurrent neural networks (RNNs) but the inherent sequential nature of the RNN models suffers from the problem of handling long sequences. |
| Approach: | They propose to employ a pre-trained BERT language model to tackle question generation tasks. |
| Outcome: | The proposed model outperforms the existing models on the question-answering dataset SQuAD and advances the BLEU 4 score from 16.85 to 22.17. |
Let Me Know What to Ask: Interrogative-Word-Aware Question Generation (D19-58)
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| Challenge: | Existing models focus on generating questions based on text and the answer to the generated question. |
| Approach: | They propose a pipelined system that predicts the type of interrogative word to be generated . they also propose qg models that can be used to generate questions based on text . |
| Outcome: | The proposed system improves on the task of QG in SQuAD, improving from 46.58 to 47.69 in BLEU-1, 17.55 to 18.53 in blu-4, 21.24 to 22.33 in METEOR, and 44.53 to 46.94 in ROUGE-L. |
Extractive NarrativeQA with Heuristic Pre-Training (D19-58)
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| Challenge: | Automated question answering (QA) from text remains a challenge for humans . a striking gap exists between machine and human performance on NLP tasks . |
| Approach: | They propose a heuristic extractive version of a data set to solve the problem of answer extraction rather than generation. |
| Outcome: | The proposed model outperforms previous models on summary-level QA from full narratives and on the METEOR metric. |
CLER: Cross-task Learning with Expert Representation to Generalize Reading and Understanding (D19-58)
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| Challenge: | In-domain datasets are used to train and validate our model, and other out-of-domain data are used for validation. |
| Approach: | They propose a model which uses cross-task learning with expert representation for the generalization of reading and understanding. |
| Outcome: | The proposed model achieved an average F1 score of 66.1 % in the out-of-domain setting, which is a 4.3 percentage point improvement over the official BERT baseline model. |
Question Answering Using Hierarchical Attention on Top of BERT Features (D19-58)
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| Challenge: | Recent advances in QA models focus on the targeted area in the passage. |
| Approach: | They propose a model which uses BERT and hierarchical attention to locate a continuous span of the passage that is the answer to the question. |
| Outcome: | The proposed model is based on a BERT embedding and a hierarchical attention model . it can locate a continuous span of the passage that is the answer to the question . |
Domain-agnostic Question-Answering with Adversarial Training (D19-58)
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| Challenge: | Adapting models to new domain without finetuning is a challenging problem in deep learning. |
| Approach: | They propose an adversarial training framework for domain generalization in Question Answering task using a conventional QA model and a discriminator. |
| Outcome: | The proposed model outperforms the baseline model on Question Answering (QA) task. |
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning (D19-58)
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| Challenge: | Existing methods focus on improving in-domain performance, leaving open the question of how they can generalize to out-of-domain and unseen RC tasks. |
| Approach: | They propose a multi-task learning framework that learns the shared representation across different tasks and builds on a large pre-trained language model and fine-tuned on multiple RC datasets. |
| Outcome: | The proposed framework improves the BERT-Large baseline by 8.39 and 7.22 respectively. |
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)
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Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang
| Challenge: | MRC requires machines to understand text and answer questions about the text. |
| Approach: | They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models. |
| Outcome: | The proposed system is ranked at top 1 of all participants in terms of averaged F1 score. |
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering (D19-58)
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| Challenge: | XLNet model is domain-agnostic for the MRQA 2019 Shared Task . a negative sampling technique is particularly effective for datasets that include unanswerable questions . |
| Approach: | They develop a domain-agnostic question answering model for the MRQA 2019 Shared Task . they use large pre-trained language models, various data sampling strategies and query and context paraphrases generated by back-translation . |
| Outcome: | The proposed model achieves second best Exact Match and F1 in the MRQA leaderboard competition. |