Papers with contrastive objective
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering (2021.findings-emnlp)
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| Challenge: | Experimental results show that our model achieves state-of-the-art results on three SQA benchmarks. |
| Approach: | They propose a self-supervised training stage and a contrastive representation learning stage for spoken question answering with auxiliary tasks and augmentation strategies. |
| Outcome: | The proposed model achieves state-of-the-art results on three SQA benchmarks. |
Contrastive Distillation on Intermediate Representations for Language Model Compression (2020.emnlp-main)
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| Challenge: | Existing methods to compress language models use a simple L_2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one. |
| Approach: | They propose a method that uses knowledge distillation to distill knowledge through intermediate layers of the teacher via a contrastive objective. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the GLUE benchmark. |
KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive Learning (2024.naacl-long)
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| Challenge: | Existing work on multimodal sentence embeddings took negative samples without reviewing, resulting in noisy and noisy negative samples. |
| Approach: | They propose a multimodal contrastive learning approach that inherits the knowledge from the teacher model to learn the difference between positive and negative instances. |
| Outcome: | The proposed approach can detect noisy and wrong negative samples before they are calculated in the contrastive objective. |
LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction Following (2023.emnlp-main)
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| Challenge: | Embodied Instruction Following has shown an impressive success rate when the environment has been seen in training, but when deployed in an unseen environment, it tends to struggle when deployed with an unsightly environment. |
| Approach: | They propose to explicitly align the agent’s hidden states with the instructions via contrastive learning to bridge the semantic gap between high-level language instructions and the agent's low-level action space. |
| Outcome: | The proposed meta-actions achieve a 4.5% success rate in unseen environments compared to a strong multi-modal Transformer baseline . |
Whodunit? Learning to Contrast for Authorship Attribution (2022.aacl-main)
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| Challenge: | Existing approaches to authorship attribution are dataset-dependent and yield inconsistent performance across corpora. |
| Approach: | They propose to fine-tune pre-trained generic language representations with a contrastive objective to learn author-specific representations by identifying clusters of authors. |
| Outcome: | The proposed approach improves on multiple human and machine authorship attribution benchmarks, but at the cost of sacrificing performance for some authors. |
Nonparametric Masked Language Modeling (2023.findings-acl)
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| Challenge: | Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases. |
| Approach: | They introduce a nonparametric masked language model that replaces a softmax with a distribution over every phrase in a reference corpus and uses an in-batch approximation to train it. |
| Outcome: | The proposed model outperforms larger parametric models on 16 tasks including classification, fact probing and question answering. |
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation? (2022.findings-naacl)
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| Challenge: | Existing methods to improve pre-training for many-to-many neural machine translation use manual cleaning of bilingual dictionaries, which are unavailable for most language pairs. |
| Approach: | They propose a word-level contrastive objective to leverage word alignments for many-to-many neural machine translation (NMT) Empirical results show that this leads to 0.8 BLEU gains for several language pairs. |
| Outcome: | Empirical results show that the proposed objective leads to 0.8 BLEU gains for several language pairs. |
MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding (2020.emnlp-main)
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| Challenge: | Existing work on phrase localization uses caption-image datasets as weak supervision . existing work on supervised phrase localisation uses a large-scale annotated dataset . |
| Approach: | They develop a multimodal alignment framework to leverage more widely available caption-image datasets to model phrase relevance. |
| Outcome: | The proposed model improves on the widely-adopted Flickr30k dataset . it also improves the previous best unsupervised result by 5.56% . |
How Transliterations Improve Crosslingual Alignment (2025.coling-main)
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Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Ayyoob ImaniGooghari, Orgest Xhelili, Haotian Ye, Chunlan Ma, François Yvon, Hinrich Schütze
| Challenge: | Recent studies show that post-aligning multilingual pretrained language models improve crosslingual alignment, but it is unclear how and why this is achieved. |
| Approach: | They propose to explicitly evaluate crosslingual alignment by adding transliterations to models using original and transliterated data. |
| Outcome: | The proposed approach improves crosslingual alignment even for random sentences. |
Attention-based Contrastive Learning for Winograd Schemas (2021.findings-emnlp)
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| Challenge: | Existing approaches to learn discriminative features using contrastive objective are lacking. |
| Approach: | They propose a self-supervised framework that leverages a contrastive loss directly at the level of self-attention. |
| Outcome: | The proposed framework outperforms all comparable unsupervised approaches while occasionally surpassing supervised ones. |
CATE: A Contrastive Pre-trained Model for Metaphor Detection with Semi-supervised Learning (2021.emnlp-main)
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| Challenge: | Existing models for metaphor detection require a large amount of labeled data and are not linguistically-based. |
| Approach: | They propose a ContrAstive pre-Trained modEl (CATE) for metaphor detection with semi-supervised learning using a pre-trained model to obtain a contextual representation of target words. |
| Outcome: | The proposed model outperforms existing models on several benchmark datasets and achieves better performance against state-of-the-art models. |
A Simple Angle-based Approach for Contrastive Learning of Unsupervised Sentence Representation (2024.findings-emnlp)
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| Challenge: | a promising baseline SimCSE has made notable breakthroughs in unsupervised SRL . however, there is still room for designing a novel contrastive framework specifically targeted for SRL. |
| Approach: | They propose an angle-based similarity function for a contrastive objective and propose a new approach for SRL. |
| Outcome: | The proposed approach shows better training dynamics on SRL than the standard cosine similarity function. |
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained Models (2022.acl-long)
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| Challenge: | Existing feature-based model selection methods are vulnerable to fine-tuning and are not reliable indicators for the PTM’s transferability. |
| Approach: | They propose feature-based model selection methods which assess PTMs’ transferability to a specific task in a fast way without fine-tuning. |
| Outcome: | The proposed methods can make FMS mistakenly judge transferability of models and can be combined with the backdoor attack to misguide the FMS to select poisoned models. |
CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning (2025.findings-emnlp)
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| Challenge: | Currently, mixture-of-experts (MoE) is underutilized on heterogeneous datasets, ignoring the fact that experts may learn similar knowledge. |
| Approach: | They propose a method to promote modularization and specialization in MoE by specializing functionalities into different experts and sparsely activating them appropriately. |
| Outcome: | The proposed method improves the capacity and specialization of mixture-of-experts (MoE) by sampling from activated and inactivated experts in top-k routing. |
Massively Multilingual Lexical Specialization of Multilingual Transformers (2023.acl-long)
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| Challenge: | Existing work focused on lexical specialization of monolingual PLMs with immense quantities of monolinguistic constraints, but recent work shows that pretrained language models can be rewired to produce high-quality word representations and perform type-level lexicals. |
| Approach: | They propose to expose massively multilingual transformers to multilingual lexical knowledge at scale using BabelNet as a source of multilingual and cross-lingual type-level lexicon knowledge. |
| Outcome: | The proposed method shows that pretrained language models can be rewired to produce high-quality word representations and perform type-level lexical tasks. |
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure (2023.emnlp-main)
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| Challenge: | Structured Autoencoder framework StrAE enables effective learning of multi-level representations through strict adherence to explicit structure. |
| Approach: | They propose a Structured Autoencoder framework that strictly adheres to explicit structure and uses a contrastive objective over tree-structured representations. |
| Outcome: | The proposed framework outperforms baselines that don’t involve explicit hierarchical compositions and is comparable to models given informative structure. |
CoLo: A Contrastive Learning Based Re-ranking Framework for One-Stage Summarization (2022.coling-1)
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| Challenge: | Existing methods for extractive and abstractive summarization use token-level or sentence-level training objectives. |
| Approach: | They propose a Contrastive Learning based re-ranking framework for one-stage summarization called CoLo. |
| Outcome: | The proposed framework boosts extractive and abstractive results on CNN/DailyMail benchmarks while maintaining inference efficiency. |
Mere Contrastive Learning for Cross-Domain Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing approaches to cross-domain sentiment analysis are labor-intensive and time-consuming. |
| Approach: | They propose a modified contrastive objective with in-batch negative samples to allow sentence representations from the same class to be pushed closer while those from the different classes become further apart in the latent space. |
| Outcome: | The proposed model can achieve state-of-the-art in cross-domain and multi-domain sentiment analysis tasks while transferring knowledge learned in the source domain to the target domain. |
Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models (2023.acl-long)
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| Challenge: | Existing methods for classification are overly confident on unseen examples . despite recent advances in NLP, some categories of distribution shift still pose serious challenges. |
| Approach: | They propose a method that generates OOD examples representative of novel classes and trains to decrease confidence on them. |
| Outcome: | The proposed method improves classifiers' ability to detect and abstain on novel class examples over previous methods by 2.3% and 5.5% over previous approaches. |
Contrastive Decoding: Open-ended Text Generation as Optimization (2023.acl-long)
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Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, Mike Lewis
| Challenge: | Using a language model, maximum probability is a poor decoding objective because it produces short and repetitive text. |
| Approach: | They propose a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint. |
| Outcome: | The proposed approach outperforms four strong decoding algorithms in automatic and human evaluations across wikipedia, news and story domains. |
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection (2023.acl-long)
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| Challenge: | Stance Detection is a task that aims to identify the attitudes of an author towards a target of interest. |
| Approach: | They propose a topic-guided diversity sampling technique and a contrastive objective to improve stance detection using the produced set. |
| Outcome: | The proposed method outperforms the state-of-the-art on 16 datasets with in-domain and out-of domain evaluations and is more generalizable with an averaged 10.2 F1 on out-domain evaluation. |
Improving Role-Oriented Dialogue Summarization with Interaction-Aware Contrastive Learning (2024.lrec-main)
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| Challenge: | Existing methods for encoding dialogues do not capture interaction information between roles, thus ignore interaction-related key information. |
| Approach: | They propose a contrastive learning based interaction-aware model for the role-oriented dialogue summarization namely CIAM and use it to train the decoder to learn role-level interaction. |
| Outcome: | The proposed model captures interaction information between different roles and produces informative summaries on two public datasets. |
Representational Isomorphism and Alignment of Multilingual Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing isomorphism of sentence representations can facilitate representational alignments in zero-shot and few-shot settings. |
| Approach: | They propose to apply a contrastive objective to LLMs with a small number of translation pairs to improve models' performance on Semantic Textual Similarity tasks. |
| Outcome: | The proposed representation-level approach significantly improves on Semantic Textual Similarity (STS) tasks across languages even without a monolingual objective. |
FairFlow: Mitigating Dataset Biases through Undecided Learning for Natural Language Understanding (2024.emnlp-main)
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| Challenge: | Existing debiasing frameworks can detect known dataset biases and spurious correlations in data. |
| Approach: | They propose a framework that learns to be undecided in its predictions for data samples . they propose 'contrary' objective that learn debiased and robust representations from biased views . |
| Outcome: | The proposed framework outperforms existing methods against out-of-domain and hard test samples without compromising performance. |