Papers with self-supervised tasks
REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training (2021.findings-acl)
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| Challenge: | Pre-trained language models have achieved great success on Machine Reading Comprehension (MRC) however, the poor support in evidence extraction hinders them from further advancing MRC. |
| Approach: | They propose a REtrieval-based pre-training approach that strengthens evidence extraction during pre-training by inherited downstream MRC tasks. |
| Outcome: | The proposed approach strengthens evidence extraction during pre-training, which is further inherited by downstream tasks. |
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks (2020.emnlp-main)
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| Challenge: | Existing methods for supervised meta-learning require many training tasks to generalize . cloze-style objectives can be used to generate a large, rich, meta-training task distribution from unlabeled text. |
| Approach: | They propose a self-supervised approach to generate a large, rich, meta-learning task distribution from unlabeled text. |
| Outcome: | The proposed approach generates a large, rich, meta-learning task distribution from unlabeled text. |
Unsupervised Adaptation of Question Answering Systems via Generative Self-training (2020.emnlp-main)
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| Challenge: | Supervised self-training methods have transformed applied machine learning . however, adapting to target data has received little attention . |
| Approach: | They propose a method to generate synthetic QA pairs for unsupervised self adaptation . they use massive amounts of data to simulate self-supervised tasks . |
| Outcome: | The proposed method improves QA systems significantly by using less data and training computation than existing augmentation approaches. |
Re-entry Prediction for Online Conversations via Self-Supervised Learning (2021.findings-emnlp)
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| Challenge: | Existing work on re-entry prediction ignores conversation thread patterns and repeated engagement of target users. |
| Approach: | They propose to use conversation thread patterns to predict whether a user will come back to a conversation they once participated in to train a model on labels that are automatically derived from the data. |
| Outcome: | The proposed task outperforms the state-of-the-art models on two social media datasets with fewer parameters and faster convergence. |
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding (2021.acl-long)
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| Challenge: | Existing models for multi-party conversation represent interlocutors and utterances individually . existing methods ignore complicated structure of MPC which may provide crucial interlocutor and tertiary semantics. |
| Approach: | They propose a pre-trained model for multi-party conversation that considers learning who says what to whom in a unified model with elaborated self-supervised tasks. |
| Outcome: | The proposed model outperforms existing models on three downstream tasks at two benchmarks. |
SiBert: Enhanced Chinese Pre-trained Language Model with Sentence Insertion (2020.lrec-1)
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| Challenge: | Recent studies show that pre-trained models can learn unsupervised language representations by self-supervised tasks on large-scale corpora. |
| Approach: | They propose a pre-training task called Sentence Insertion for Chinese query-passage pairs NLP tasks . they propose 'word segmentation' method to enhance Chinese Bert performance . |
| Outcome: | The proposed task improves Chinese pre-trained models significantly. |
Tri-Train: Automatic Pre-Fine Tuning between Pre-Training and Fine-Tuning for SciNER (2020.findings-emnlp)
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| Challenge: | Pre-training a language model by self-supervised tasks on huge datasets and fine-tuning with small labelled data are often inadequate for scientific NER tasks. |
| Approach: | They propose to introduce a "pre-fine tuning" step between pre-training and fine-tuning to construct a corpus by selecting sentences from unlabeled documents that are the most relevant with labelled training data. |
| Outcome: | The proposed approach improves on seven benchmarks on the performance of the proposed model on labelled datasets. |
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP (2021.emnlp-main)
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| Challenge: | Meta-learning considers learning as an efficient learning process that can leverage its past experience to accurately solve new tasks. |
| Approach: | They propose to provide task distributions for meta-learning by considering self-supervised tasks automatically proposed from unlabeled text to enable large-scale meta- learning in NLP. |
| Outcome: | The proposed distributions show that human learning models perform better on the few-shot benchmark than previous methods. |
How to Find Strong Summary Coherence Measures? A Toolbox and a Comparative Study for Summary Coherence Measure Evaluation (2022.coling-1)
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| Challenge: | Existing methods to evaluate summary coherence are often evaluated using disparate datasets and metrics. |
| Approach: | They propose to use automatic evaluation to evaluate coherence of summaries by selecting high-scoring candidates. |
| Outcome: | The proposed methods show that they can perform better on an even playing field. |
Unleashing the Power of Language Models in Text-Attributed Graph (2023.findings-emnlp)
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| Challenge: | Existing studies on graph learning on text-attributed graphs have been limited by memory cost and underutilization of relationships between nodes and words. |
| Approach: | They propose a Node Representation Update Pre-training Architecture based on Co-modeling text and graph to learn representations of papers and words simultaneously. |
| Outcome: | The proposed model outperforms baselines on the ogbn-arxiv benchmark dataset. |
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding (2022.acl-long)
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Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, Shiliang Pu, Fei Wu
| Challenge: | Existing methods for grounding video frames with dense annotations require enormous amount of human effort. |
| Approach: | They propose to ground natural language in video frames with only one frame labeled . they propose an end-to-end model that eliminates interference of irrelevant frames . |
| Outcome: | The proposed model can ground natural language in all video frames with only one frame labeled . the proposed model eliminates interference of irrelevant frames based on branch search and cropping techniques . |
Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing (2020.acl-main)
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| Challenge: | Existing semantic parsing frameworks rely on nontrivial human labor to generate canonical utterances. |
| Approach: | They propose a framework that uses an unsupervised paraphrase model to parse canonical utterances. |
| Outcome: | The proposed framework is effective and compatible with supervised training. |
Pre-training Mention Representations in Coreference Models (2020.emnlp-main)
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| Challenge: | Existing methods to improve coreference resolution use labeled data. |
| Approach: | They propose two self-supervised tasks that are closely related to coreference resolution to improve mention representation. |
| Outcome: | The proposed models improve mention representations by learning them on a GAP dataset. |
SSP: Self-Supervised Post-training for Conversational Search (2023.findings-acl)
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| Challenge: | Existing methods to search for information are limited by data scarcity and lack contextual semantic understanding. |
| Approach: | They propose a new post-training paradigm with three self-supervised tasks to initialize the conversational search model to enhance dialogue structure and contextual semantic understanding. |
| Outcome: | The proposed method can initialize the conversational search model to enhance dialogue structure and contextual semantic understanding. |