| Challenge: | a study shows that training reading comprehension models assumes that the training instances are independent and identically distributed . however, this assumption can cause the learner to ignore distinguishing cues between related or minimally different questions . |
| Approach: | They propose to normalize question-answer scores across neighborhoods of closely contrasting questions and/or answers by adding a cross entropy loss term to the supervision signal. |
| Outcome: | The proposed methods show up to 9% absolute gains in accuracy on two datasets. |
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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. |
Single-dataset Experts for Multi-dataset Question Answering (2021.emnlp-main)
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| Challenge: | Prior work has focused on training one network on multiple datasets to build a model that performs well on all of the training datasets and generalizes and transfers better to new datasets. |
| Approach: | They combine multiple reading comprehension datasets to build a multi-dataset question answering model with an ensemble of single-data set experts. |
| Outcome: | The proposed model outperforms baseline models in in-distribution accuracy and generalization and transfer performance. |
Contextualized Word Representations for Reading Comprehension (N18-2)
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| Challenge: | Reading comprehension (RC) is a high-level task in natural language understanding that requires reading a document and answering questions about its content. |
| Approach: | They propose to provide a standard neural network for reading a document and answering a question about its content. |
| Outcome: | The proposed model improves on the competitive SQuAD dataset by providing rich contextualized word representations and allowing it to choose between context-dependent and context-independent representations. |
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. |
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MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension (P19-1)
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| Challenge: | A large number of reading comprehension (RC) datasets have been created, but little research has been done on whether they generalize to one another and the extent to which existing datasets can be leveraged for improving performance on new ones. |
| Approach: | They propose a BERT-based reading comprehension model that can be trained on multiple RC datasets. |
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CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction (2021.acl-long)
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| Challenge: | Existing methods to reduce noise from DS generated training data are not effective for distantly supervised relation extraction (DSRE) |
| Approach: | They propose a multi-instance learning framework to reduce DS noise by dividing training instances into several bags and using them as new data units. |
| Outcome: | The proposed framework improves on NYT10, GDS and KBP with significant improvements over existing methods. |
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks (D18-1)
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| Challenge: | Recent research addresses reading comprehension, where examples consist of (question, passage, answer) tuples. |
| Approach: | They establish sensible baselines for bAbI, SQuAD, CBT, CNN and Who-did-What datasets and compare them to their previous work. |
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Self-Supervised Test-Time Learning for Reading Comprehension (2021.naacl-main)
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| Challenge: | Recent work on unsupervised question answering shows that models can be trained with procedurally generated question-answer pairs and achieve performance competitive with supervised methods. |
| Approach: | They propose a method that performs "test-time learning" on a given context . they use self-supervision to train models on synthetically generated question-answer pairs . |
| Outcome: | The proposed method outperforms current unsupervised methods and outperformed supervised methods. |
Instance-Based Neural Dependency Parsing (2021.tacl-1)
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Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Masashi Yoshikawa, Kentaro Inui
| Challenge: | Existing models that use instance-based inference for dependency parsing are difficult to understand for humans. |
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QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization (D19-1)
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| Challenge: | Existing models are not good at distinguishing distractor sentences which look related but do not answer the question. |
| Approach: | They propose a method to regularize question answering models by maximizing mutual information among passages, questions, and answers. |
| Outcome: | The proposed model achieves state-of-the-art on the Adversarial-SQuAD dataset. |