Papers with similarity scores
Exploiting WordNet Synset and Hypernym Representations for Answer Selection (2020.aacl-main)
Copied to clipboard
| Challenge: | Answer selection (AS) is a challenging subtask of document-based question answering (DQA). |
| Approach: | They propose to use WordNet to enrich the word representation and sentence encoding to incorporate similarity scores of two concepts that share synset or hypernym relations into the attention mechanism. |
| Outcome: | The proposed model outperforms existing state-of-the-art models on the public WikiQA and SelQA datasets and significantly improves the baseline system. |
In-context Learning and Gradient Descent Revisited (2024.naacl-long)
Copied to clipboard
| Challenge: | In-context learning (ICL) has shown impressive results in few-shot learning tasks, yet its underlying mechanism remains elusive. |
| Approach: | They propose a simple gradient descent-based optimization procedure that respects layer causality and improves similarity scores significantly. |
| Outcome: | The proposed procedure improves similarity scores on untrained models despite not showing ICL. |
Thesis Proposal: Auditing and Mitigating Demographic Bias in Multi-Stage Retrieval Systems for Criminal Justice Applications (2026.acl-srw)
Copied to clipboard
| Challenge: | racial descriptors alter embedding similarity scores and retrieval rankings, a new study shows . rife-specific biases can displace relevant records outside top-10 results, the study concludes . |
| Approach: | They propose to detect, measure, and mitigate racial bias in NLP systems deployed in criminal justice contexts . they propose to develop and evaluate debiasing techniques, validate synthetic findings on authentic law enforcement data . |
| Outcome: | The proposed research examines how bias propagates across retrieval pipelines . it shows that racial descriptors alter embedding similarity scores and retrieval rankings . |
Describing Sets of Images with Textual-PCA (2022.findings-emnlp)
Copied to clipboard
| Challenge: | a new method to describe images using a common theme is needed to describe the images . a grammatical phrase is not sufficient to describe an image set, since captioning engines are not general enough. |
| Approach: | They propose a method to capture attributes of images and variations within a set . they use a pretrained vision-language model to generate a centroid phrase with the largest average similarity . |
| Outcome: | The proposed method captures the essence of image sets and describes them in a semantically meaningful way . it is easy for humans to identify and describe a common theme, but it is not generic enough . |
Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets (2022.lrec-1)
Copied to clipboard
| Challenge: | Technical logbook data typically has both a domain, the field it comes from, and an application, what it is used for. |
| Approach: | They propose to use domain-specific technical language to identify technical logbook entries by using transfer learning to learn from different domains and from different datasets. |
| Outcome: | The proposed approach improves performance in all cases but one of the three domains studied. |
High-Order Semantic Alignment for Unsupervised Fine-Grained Image-Text Retrieval (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies focus on learning global or local correspondence, but lack fine-grained local-global alignment. |
| Approach: | They propose a High Order Semantic Alignment (HOSA) model that can provide complementary and comprehensive semantic clues to infer correlation scores. |
| Outcome: | The proposed model outperforms state-of-the-art models in retrieving the most relevant results. |
ODASim: Ordered, Distinctive and Absolute Semantic Similarity for Code Explanation Evaluation (2026.findings-acl)
Copied to clipboard
Prince Kumar, Vitobha Munigala, Jaydeep Sen, Ashish Mittal, Vishwajeet Kumar, Srikanth G. Tamilselvam
| Challenge: | Existing methods for code explanations fail to distinguish correct from partially or fully incorrect explanations and their similarity scores are poorly calibrated. |
| Approach: | They propose a model-agnostic graded fine-tuning framework that learns calibrated similarity representations between code and explanations to support fine-grained supervision and evaluation. |
| Outcome: | The proposed framework improves F1 score and ECE scores on two embedding models and reduces expected calibration error. |
Improving the Quality of Web-mined Parallel Corpora of Low-Resource Languages using Debiasing Heuristics (2025.emnlp-main)
Copied to clipboard
| Challenge: | Parallel Data Curation (PDC) techniques aim to filter out noisy parallel sentences from web-mined corpora. |
| Approach: | They propose to rank parallel sentences using similarity scores on sentence embeddings derived from Pre-trained Multilingual Language Models (multiPLMs) . previous research has shown that the choice of multiPLM significantly impacts the quality of the filtered parallel corpus. |
| Outcome: | The proposed methods reduce disparities between multiPLMs while producing better results. |
One Single Hub Text Breaks CLIP: Identifying Vulnerabilities in Cross-Modal Encoders via Hubness (2026.acl-long)
Copied to clipboard
| Challenge: | et al., 2010) show that hub embeddings are close to many unrelated examples in high-dimensional embeddable spaces . cross-modal encoders that project different modalities into a shared space are useful for cross-module applications . |
| Approach: | They propose a method for identifying the hub embedding and its corresponding hub text . they use images to evaluate cross-modal encoders that project different modalities into a shared space . |
| Outcome: | The proposed method can identify a single hub embedding and its corresponding hub text . it achieves comparable or higher similarity scores than human-written reference captions in many images . |