| 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. |
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| Challenge: | Existing question generation methods rely on large amounts of synthetically generated datasets and costly computational resources. |
| Approach: | They propose a framework for domain adaptation that combines question generation and domain-invariant learning to answer out-of-domain questions in settings with limited text corpora. |
| Outcome: | The proposed framework improves on state-of-the-art questions in a domain with limited text corpora. |
Domain Adaptation for Question Answering via Question Classification (2022.coling-1)
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| Challenge: | Question answering systems often experience performance deterioration upon user-generated questions. |
| Approach: | They propose a question classification framework to help QA domains adapt to different domains. |
| Outcome: | The proposed framework improves on state-of-the-art datasets against multiple datasets. |
Towards Robust Extractive Question Answering Models: Rethinking the Training Methodology (2024.findings-emnlp)
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| Challenge: | Existing models lack robustness against distribution shifts and adversarial attacks when training on unanswerable questions in EQA datasets. |
| Approach: | They propose a novel loss function for the EQA problem to improve the robustness of extractive question answering models by adding adversarial questions to a crowdsourcing process. |
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Domain Adaptation for Subjective Induction Questions Answering on Products by Adversarial Disentangled Learning (2024.acl-long)
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| Challenge: | Existing methods to answer subjective questions about products are often imbalanced across product domains. |
| Approach: | They propose a domain-adaptive model that integrates multiple viewpoints into a good answer by integrating these heterogeneous and inconsistent viewpoints. |
| Outcome: | The proposed model integrates multiple viewpoints into a single answer span and is able to integrate them into the answer. |
Robust Semantic Parsing with Adversarial Learning for Domain Generalization (N19-2)
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| Challenge: | Using adversarial learning to train models on a higher level of abstraction to increase their robustness to lexical and stylistic variations is crucial for the integration of Semantic Parsing technologies in real applications. |
| Approach: | They propose to perform Semantic Parsing with a domain classification adversarial task and an unsupervised domain discovery approach that yields equivalent improvements. |
| Outcome: | The proposed approach improves on a French corpus of encyclopedic documents annotated with FrameNet and an unsupervised domain discovery approach yields equivalent improvements. |
Learning to Collaborate for Question Answering and Asking (N18-1)
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| Challenge: | Question answering (QA) and question generation (QG) are closely related tasks. |
| Approach: | They propose a training algorithm that generalizes both Generative Adversarial Network and Generating Domain-Adaptive Nets under the question answering scenario. |
| Outcome: | The proposed training algorithm generalizes both Generative Adversarial Network (GAN) and Generating Domain-Adaptive Nets (GDAN) under the question answering scenario. |
Meta-Learning for Domain Generalization in Semantic Parsing (2021.naacl-main)
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| Challenge: | Existing approaches to parsing use standard supervised learning, but little attention has been given to domain generalization. |
| Approach: | They propose a meta-learning framework which targets zero-shot domain generalization for semantic parsing. |
| Outcome: | The proposed framework significantly boosts parser performance on English and Chinese spider datasets. |
What’s in a Domain? Learning Domain-Robust Text Representations using Adversarial Training (N18-2)
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| Challenge: | a key roadblock is application to new domains, unseen in training. |
| Approach: | They propose a method to optimise in- and out-of-domain accuracy by combing domain-specific and domain-general components with adversarial training for domain. |
| Outcome: | The proposed method improves on domain adaptation and domain-adversarial training. |
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. |
Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension (2020.findings-emnlp)
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| Challenge: | Reading comprehension models often overfit to nuances of training datasets and fail at adversarial evaluation. |
| Approach: | They propose a method that introduces multiple points of confusion within the context and shows dependence on insertion location of the distractor. |
| Outcome: | The proposed methods improve robustness against adversarial evaluation but weak generalization to the source domain and new domains and languages. |