Papers with auxiliary loss function
Improving Vietnamese-English Cross-Lingual Retrieval for Legal and General Domains (2025.naacl-short)
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Toan Ngoc Nguyen, Nam Le Hai, Nguyen Doan Hieu, Dai An Nguyen, Linh Ngo Van, Thien Huu Nguyen, Sang Dinh
| Challenge: | Existing document retrieval systems focus on a single language, targeting resource-rich languages like English or Chinese. |
| Approach: | They propose auxiliary loss function and symmetrical training strategy for cross-lingual retrieval between Vietnamese and English . they propose a dataset that covers the general domain and extends to the legal field . |
| Outcome: | The proposed dataset significantly improves state-of-the-art models on cross-lingual retrieval tasks. |
Improving Pretraining Techniques for Code-Switched NLP (2023.acl-long)
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| Challenge: | Multilingual pretraining models for code-switched inputs are a key component of NLP applications. |
| Approach: | They propose to use masked language modeling techniques to mask code-switched text that are cognizant of language boundaries prior to masking. |
| Outcome: | The proposed techniques improve performance on two downstream tasks, Question Answering (QA) and Sentiment Analysis (SA), compared to standard pretraining techniques. |
Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Experimental results show document-level neural machine translation improves lexical consistency . inconsistent translations tend to confuse readers in some cases . |
| Approach: | They propose to use a word link to obtain a document word link and an auxiliary loss function to constrain that their translation should be consistent. |
| Outcome: | The proposed approach improves translation consistency on ChineseEnglish and EnglishFrench translation tasks. |
Sneaking Syntax into Transformer Language Models with Tree Regularization (2025.naacl-long)
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| Challenge: | Existing methods for incorporating syntactic inductive biases into transformers are limited . we introduce auxiliary loss function that converts bracketing decisions into differentiable orthogonality constraints on vector hidden states. |
| Approach: | They propose to introduce syntactic inductive biases into transformer circuits through a structured regularizer. |
| Outcome: | The proposed approach could unlock more robust and data-efficient learning in transformer language models . it integrates seamlessly with the standard LM objective, requiring no architectural changes. |
Guiding Attention for Self-Supervised Learning with Transformers (2020.findings-emnlp)
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| Challenge: | Recent studies show that self-attention patterns in trained models contain a majority of non-linguistic regularities. |
| Approach: | They propose a technique to allow efficient self-supervised learning with bi-directional Transformers by using an auxiliary loss function to guide attention heads to conform to such patterns. |
| Outcome: | The proposed method achieves state-of-the-art in low-resource settings and is agnostic to pre-training objectives. |
Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience (2021.emnlp-main)
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| Challenge: | Pretrained transformer-based language models have demonstrated state-of-the-art predictive performance when adapted into a range of language understanding tasks. |
| Approach: | They propose to use salient information extracted a priori from training data to complement the task-specific information learned by the model during fine-tuning on a downstream task. |
| Outcome: | The proposed model can provide more faithful explanations across four different feature attribution methods compared to vanilla BERT. |