Papers with reading comprehension tasks
Technical Question Answering across Tasks and Domains (2021.naacl-industry)
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| Challenge: | Existing methods for technical QA have a limited data size and question and answer overlaps . |
| Approach: | They propose a framework of deep transfer learning to address technical QA across tasks and domains using document retrieval and reading comprehension tasks. |
| Outcome: | The proposed framework performs better than state-of-the-art methods on the TechQA task. |
EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning (2024.naacl-short)
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| Challenge: | Language models are adopting inference-time prompting techniques such as zero-shot and few-shot prompting. |
| Approach: | They propose a prompting technique that prompts the model to rephrase its queries before answering them. |
| Outcome: | The proposed prompt improves zero-shot-CoT performance of code-davinci-002 by 5% . the proposed prompt is tailored for four scenarios in both zero- and few-shot settings . |
Reading Comprehension with Graph-based Temporal-Casual Reasoning (C18-1)
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| Challenge: | Existing methods for reading comprehension tasks ignore semantic relations between sentences or use sliding window scanning over the words of the passage without sentence breaks. |
| Approach: | They propose a method to integrate information from multiple sentences to answer complex questions. |
| Outcome: | Experiments on RACE and MCTest show that the proposed approach improves state-of-the-art methods on simple factoid questions. |
VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension (2024.eacl-long)
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| Challenge: | Existing datasets for machine reading comprehension tasks in Vietnamese focus on written documents, such as Wikipedia articles, online newspapers, or textbooks. |
| Approach: | They propose to capture Vietnamese spoken language in natural settings and use it to create a machine-learning corpus for machine reading comprehension tasks. |
| Outcome: | The proposed corpus consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube . |
Entity Tracking Improves Cloze-style Reading Comprehension (D18-1)
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| Challenge: | Recent work on reading comprehension tasks has improved with simple approaches, but still trail human performance. |
| Approach: | They propose to add additional entity features and a multi-task tracking objective to improve model performance . they compare the model's predictions with those of more complicated models . |
| Outcome: | The proposed model outperforms the current state of the art on the LAMBADA dataset by 8 pts. |
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding (2021.acl-long)
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| Challenge: | Pre-trained language models are vulnerable to simple perturbations, causing poor robustness . recent studies show that adversarial training is useless or harmful for the model to detect these semantic changes. |
| Approach: | They propose to use adversarial training to improve the robustness of pre-trained models . they propose to construct negative examples with similar and opposite semantics . |
| Outcome: | Empirical results show that the proposed approach improves on sentiment analysis, reasoning, and reading comprehension tasks. |
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation (2020.coling-main)
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| Challenge: | Various BERT-based reading comprehension models have been proposed, however, these models employ the combined input method without further modification for reading comprehension. |
| Approach: | They propose a BERT-based interactive machine reading comprehension model that uses BERT's combined input method without further modification for reading comprehension. |
| Outcome: | The proposed model improves reading comprehension tasks compared to BERT-based models. |
Learning Dense Representations of Phrases at Scale (2021.acl-long)
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| Challenge: | Existing phrase retrieval models rely on sparse representations and still underperform retriever-reader approaches. |
| Approach: | They propose a method to learn phrase representations from reading comprehension tasks using negative sampling methods. |
| Outcome: | The proposed model improves over previous models by 15%-25% absolute accuracy and matches the performance of state-of-the-art retrieval models. |
Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model (D19-1)
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| Challenge: | Existing studies on RC datasets in English have limited results due to lack of training data. |
| Approach: | They systematically explore zero-shot cross-lingual transfer learning on reading comprehension tasks with pre-trained language representation model. |
| Outcome: | The proposed model performs well on reading comprehension tasks on pre-trained language representation models. |
Chunk, Align, Select: A Simple Long-sequence Processing Method for Transformers (2024.acl-long)
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| Challenge: | Existing transformer-based models struggle with long-sequence processing due to computational costs . a framework to enhance long-content processing of transformers is proposed . |
| Approach: | They propose a framework to enhance long-sequence processing of transformers by three steps . they demonstrate that the framework significantly outperforms prior long-quence processors . |
| Outcome: | The proposed framework outperforms baseline models on long-sequence summarization and reading comprehension tasks. |
Minding Language Models’ (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker (2023.acl-long)
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| Challenge: | Empirical results show plug-and-play approach to reason about belief states of multiple characters in reading comprehension tasks is more precise and interpretable than previous approaches. |
| Approach: | They propose a plug-and-play approach to reason about the belief states of multiple characters in reading comprehension tasks via explicit symbolic representation. |
| Outcome: | The proposed algorithm improves theory of mind of off-the-shelf neural language models without supervision. |
A linguistically-motivated evaluation methodology for unraveling model’s abilities in reading comprehension tasks (2024.emnlp-main)
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| Challenge: | Existing models fail for linguistic characteristics of input examples, despite the impressive quantity of scientific studies dedicated to them, the capabilities, limitations, and risks of these models remain largely unknown. |
| Approach: | They propose to use semantic frame annotation to characterize examples by a small number of complexity factors to account for model’s difficulty. |
| Outcome: | The proposed evaluation methodology is based on the intuition that certain examples consistently yield lower scores regardless of model size or architecture. |