Papers with similarity metric
Attention with Dependency Parsing Augmentation for Fine-Grained Attribution (2025.findings-acl)
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| Challenge: | Existing fine-grained attribution methods rely on model-internal similarity metrics but lack a fine-grain representation of the data. |
| Approach: | They propose to use model-internal similarity metrics to validate RAG-generated content . they aggregate token-wise evidence through set union operations and integrate dependency parsing to enrich the semantic completeness of target spans. |
| Outcome: | The proposed method outperforms all prior works in the validation of RAG-generated content. |
A Rank-Based Similarity Metric for Word Embeddings (P18-2)
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| Challenge: | Word Embeddings have become a standard for word representations, with vector cosine being the only similarity metric. |
| Approach: | They propose to use rank-based similarity estimation metrics to measure word similarity . they find WE outperforms vector cosine in the recent outlier detection task . |
| Outcome: | The proposed rank-based measure outperforms vector cosine in the recent outlier detection task. |
Principled Paraphrase Generation with Parallel Corpora (2022.acl-long)
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| Challenge: | a popular method for paraphrase generation is round-trip machine translation (MT). |
| Approach: | They propose a method that formalizes the implicit similarity function and relaxes it by requiring the entire translation distribution to match . they propose MT can be used to generate paraphrases by decoding back to the source without having to generate pivot translations. |
| Outcome: | The proposed approach is more principled and efficient than round-trip machine translation (MT) and offers an adjustable parameter to control the fidelity-diversity trade-off. |
How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning (2024.emnlp-main)
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| Challenge: | In-context learning (ICL) is an emergent ability of large language models. |
| Approach: | They propose to use in-context learning to predict sentences with semantically-unrelated labels on 1% heads to investigate the mechanism. |
| Outcome: | The proposed methods reduce the majority label bias and recency bias by 22% and 17%, respectively. |
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing methods for Named Entity Recognition (NER) use a similarity metric to measure semantic similarity between test samples and referents, but their performance is limited due to the label scarcity. |
| Approach: | They propose a novel approach to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class. |
| Outcome: | The proposed approach outperforms state-of-the-art models with a significant margin in most cases. |
Meta-Learning to Compositionally Generalize (2021.acl-long)
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| Challenge: | Existing studies show that neural networks struggle with compositional generalization . prior work asserts that there are fundamental differences between cognitive and connectionist architectures that make compositional globalization unlikely. |
| Approach: | They propose a meta-learning augmented version of supervised learning that optimizes for out-of-distribution generalization. |
| Outcome: | The proposed model improves generalization performance on COGS and SCAN datasets. |
An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text (2020.findings-emnlp)
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| Challenge: | Existing approaches to parse scientific text using grammatically similar labeled sentences are limited and expensive to create. |
| Approach: | They propose a method where semantic labels from structurally similar sentences are copied to test sentences. |
| Outcome: | The proposed approach outperforms baseline and prior methods by 0.75 to 3 F1 absolute in the wet lab protocol corpus and 1 F1 absolut in the materials science procedural text corpus. |
Enhancing the Ranking Context of Dense Retrieval through Reciprocal Nearest Neighbors (2023.emnlp-main)
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| Challenge: | Sparse annotation poses persistent challenges to training dense retrieval models . despite potential future endeavors to extend annotation, issue of false negatives persists . |
| Approach: | They propose a method that smooths out the annotation of unlabeled relevant documents . they use reciprocal nearest neighbors to estimate relevance and rerank candidates . |
| Outcome: | The proposed method reduces the issue of false negatives in contrastive learning by reducing sparsity. |
Evaluating Design Decisions for Dual Encoder-based Entity Disambiguation (2025.acl-long)
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| Challenge: | Entity disambiguation (ED) is the task of linking mentions in text to corresponding entries in a knowledge base. |
| Approach: | They propose a document-level Dual Encoder model that embeds mentions and label candidates in a shared embedding space and applies a similarity metric to predict the correct label. |
| Outcome: | The proposed model improves the disambiguation of ambiguous mentions of entities in text to their respective KB entries. |
Measuring Cross-Text Cohesion for Segmentation Similarity Scoring (2024.lrec-main)
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| Challenge: | a new segmentation similarity metric is being developed for text segmentation . current metrics are content-agnostic and do not provide fine-grained scoring . |
| Approach: | They propose a word-embedding-based metric of cross-textual cohesion based on the formal linguistic definition of cohetion and incorporate it into a new segmentation similarity metric, SED. |
| Outcome: | The proposed metric provides fine-grained segmentation similarity scoring for 3 basic errors, avoiding limitations of traditional metrics. |
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model (2023.findings-emnlp)
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| Challenge: | kNN-LM, REALM, DPR + FiD, Contriever + ATLAS, and Contriver + Flan-T5 are popular retriever-augmented language models for a variety of tasks. |
| Approach: | They evaluate the strengths and weaknesses of kNN-LM, REALM, DPR + FiD, Contriever + ATLAS, and Contriver + Flan-T5 in reasoning over retrieved statements across different tasks. |
| Outcome: | The proposed models do not exhibit strong reasoning even when provided with only the required statements. |
Adversarial Metric Learning for Fine-Grained Emotion Classification (2026.acl-long)
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| Challenge: | Recent advances in fine-grained emotion classification relied on contrastive learning with hard-pair mining. |
| Approach: | They propose an adversarial metric learning framework that replaces fixed similarity metrics with a learnable metric family and trains representations to remain discriminative under worst-case similarity distortions. |
| Outcome: | The proposed framework trains a pairwise discriminator to maximally confuse two hard pair types while training the encoder to remain discriminative under worst-case similarity distortions. |