Papers with self-supervised training
Deploying a Retrieval based Response Model for Task Oriented Dialogues (2022.emnlp-industry)
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| Challenge: | a task-oriented dialogue system needs high conversational capability and can be easily adaptable to changing situations. |
| Approach: | They propose a retrieval-based conversational model that can rank a large set of responses . they propose supervised training and fine-tuning on limited data collected through a human-in-the-loop platform . |
| Outcome: | The proposed model can scale to rank a large set of responses in real-world situations. |
A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment (2021.acl-long)
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| Challenge: | Existing methods for learning word alignment include statistical word aligners (e.g. GIZA++) Existing word alignment models employ a target-to-source attention mechanism which can provide rough word alignments but with a low accuracy. |
| Approach: | They propose a bidirectional Transformer based alignment model for unsupervised learning of the word alignment task. |
| Outcome: | The proposed model outperforms both previous neural word alignment approaches and the popular statistical word aligner GIZA++ on three word alignment tasks. |
Where is the answer? An empirical study of positional bias for parametric knowledge extraction in language model (2025.naacl-long)
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| Challenge: | Language model (LM) stores diverse factual knowledge in their parameters, which is learned during self-supervised training on unlabeled documents. |
| Approach: | They investigate the issue of "perplexity curse" in the continued training of language model (LM) they find that all studied LMs suffer from positional bias in the training document . |
| Outcome: | The proposed model is able to extract information from multiple questions with diverse queries. |
Multi-Modal Bias: Introducing a Framework for Stereotypical Bias Assessment beyond Gender and Race in Vision–Language Models (2023.eacl-main)
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| Challenge: | Recent advances in self-supervised training have led to a new class of pretrained vision–language models. |
| Approach: | They propose a visual and textual bias benchmark to assess bias in self-supervised multimodal models using 3,800 images and phrases from 14 population subgroups. |
| Outcome: | The proposed model shows that it favors certain groups while maintaining the accuracy of the model. |
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale (2020.emnlp-main)
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| Challenge: | a new study examines the zero-shot transfer capabilities of text matching models on a massive scale. |
| Approach: | They propose to integrate self-supervised with supervised multi-task learning on all available source domains to study the zero-shot transfer capabilities of text matching models on a massive scale. |
| Outcome: | The proposed model outperforms in-domain BERT and the previous state of the art on six benchmarks. |
Direct Metric Optimization for Image Captioning through Reward-Weighted Augmented Data Utilization (2024.acl-long)
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Takumi Takada, Yuma Suzuki, Hiroki Takushima, Hayato Tanoue, Haruki Sato, Aiswariya Kumar, Hiroki Nishihara, Takayuki Hori, Kazuya Ueki
| Challenge: | Recent large-scale vision language models (VLMs) lack continuity between learning objective and performance metrics. |
| Approach: | They propose a lightweight final-metric-optimizing training method that replaces the expensive exploration process in RL with an offline, diverse text data augmentation method. |
| Outcome: | The proposed method achieves comparable performance to state-of-the-art RL method while saving hundreds of times more model forwarding iterations and greater amounts of computation time. |
QuASAR: A Question-Driven Structure-Aware Approach for Table-to-Text Generation (2025.acl-long)
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| Challenge: | Existing methods for table-to-text generation fail to capture the structure of tabular data or rely on complex attention mechanisms, limiting their applicability. |
| Approach: | They propose a question-driven self-supervised approach to enhance the model’s structural perception and representation capabilities by focusing on structure-related queries. |
| Outcome: | The proposed model improves its model's structural perception and representation capabilities by guiding it to capture local and global table structures. |