Papers with relatedness
Similar, but why? A Toolkit for Explaining Text Similarity (2026.eacl-demo)
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| Challenge: | XPLAINSIM is a Python package that explains textual similarity in an easy-to-use way. |
| Approach: | They propose a Python package that unifies three approaches to explain text similarity . they demonstrate the value of the package through intuitive examples and empirical research . |
| Outcome: | XPLAINSIM is a Python package that unifies three approaches to explain text similarity . the authors show that the package is useful for explaining text similarities in a simple way . |
Can Network Embedding of Distributional Thesaurus Be Combined with Word Vectors for Better Representation? (N18-1)
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| Challenge: | Distributed representations of words learned from text have proved to be successful in various natural language processing tasks. |
| Approach: | They propose to embed a distributional thesaurus network into dense word vectors and compare them to state-of-the-art word representations. |
| Outcome: | The proposed representations improve performance against state-of-the-art word representations even without handcrafted lexical resources. |
What Makes Sentences Semantically Related? A Textual Relatedness Dataset and Empirical Study (2023.eacl-main)
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| Challenge: | Existing work on semantic relatedness has focused on semantic similarity because of a lack of relatedness datasets. |
| Approach: | They propose a dataset for semantic relatedness that has 5,500 English sentence pairs manually annotated using a comparative annotation framework. |
| Outcome: | The proposed dataset has 5,500 English sentence pairs manually annotated using a comparative annotation framework. |
Persona Expansion with Commonsense Knowledge for Diverse and Consistent Response Generation (2023.eacl-main)
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Donghyun Kim, Youbin Ahn, Wongyu Kim, Chanhee Lee, Kyungchan Lee, Kyong-Ho Lee, Jeonguk Kim, Donghoon Shin, Yeonsoo Lee
| Challenge: | Existing researches have focused on generating diverse and consistent responses based on personal traits. |
| Approach: | They propose a consistent persona expansion framework that improves not only the diversity but also the consistency of persona-based responses. |
| Outcome: | The proposed framework improves not only the diversity but also the consistency of persona-based responses on the Persona-Chat dataset. |
Knowing the Author by the Company His Words Keep (L18-1)
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| Challenge: | In traditional linguistics, there exists a famous saying that one should know a word by the company it keeps. |
| Approach: | They propose a method which uses word embeddings to identify pairwise relational features in the context of authorship attribution. |
| Outcome: | The proposed method is based on three literary corpora and shows that word similarity is a key feature in the authorship attribution task. |
Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual Alignment (2025.acl-long)
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| Challenge: | Multilingual sentence encoders are often trained to map sentences from different languages into a shared semantic vector space . cross-lingual alignment training distorts optimal monolingual structure of semantic spaces of individual languages . a modular solution can be used for cross-linguistic tasks such as cross-language semantic similarity and zero-shot transfer . |
| Approach: | They propose a modular training system that embeds sentences from different languages into a shared semantic vector space. |
| Outcome: | The proposed solution achieves better performance across all tasks compared to monolithic models. |
To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic Relatedness (P18-1)
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| Challenge: | Recent success of Recurrent Neural Networks (RNNs) in Machine Translation (MT) has prompted attention mechanisms to be used in machine translation. |
| Approach: | They propose a tree-structured attention model on Tree Long Short-Term Memory Networks . they also experiment with three LSTM variants: bidirectional-LSTMs, Constituency Tree-LSTS, and Dependency Tree LSTS. |
| Outcome: | The proposed model is based on tree-LSTMs, constituency trees, and dependencies trees. |
Relational Word Embeddings (P19-1)
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| Challenge: | Existing approaches to learn word embeddings rely on external knowledge bases . however, they are limited by the amount of available relational knowledge . |
| Approach: | They propose to encode relational knowledge in a separate word embedding . this is complementary to a standard word embedded from co-occurrence statistics . |
| Outcome: | The proposed word embedding is complementary to a standard word embed. |
Graph Neural Network Enhanced Retrieval for Question Answering of Large Language Models (2025.naacl-long)
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| Challenge: | Existing retrieval methods divide reference documents into passages, treating them in isolation. Existing methods only use contiguous passages or keywords. |
| Approach: | They propose a retrieval method that leverages graph neural networks to exploit relatedness between passages to enhance retrieval. |
| Outcome: | The proposed method improves retrieval by exploiting the relatedness between passages. |
DeltaScore: Fine-Grained Story Evaluation with Perturbations (2023.findings-emnlp)
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| Challenge: | Existing evaluation metrics for stories are limited in assessing intricate aspects of storytelling, such as fluency and interestingness. |
| Approach: | They propose a novel method that uses perturbation techniques to evaluate story aspects . they compare fluency, coherence, relatedness, logicality, interestingness and interestingness to existing metrics . |
| Outcome: | The proposed method shows that one specific perturbation is highly effective in capturing multiple aspects. |
Semantic Relatedness of Wikipedia Concepts – Benchmark Data and a Working Solution (L18-1)
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| Challenge: | Existing methods to measure relatedness between Wikipedia concepts are lacking. |
| Approach: | They propose a new type of concept relatedness dataset, WORD, which is annotated by a human . they use this dataset to assess relatedness between Wikipedia concepts using supervised methods. |
| Outcome: | The proposed dataset outperforms existing methods for measuring relatedness between Wikipedia concepts. |
Rationale-based Opinion Summarization (2024.naacl-long)
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| Challenge: | Existing methods to generate concise summaries of reviews are generic and lack supporting details. |
| Approach: | They propose a rationale-based opinion summarization paradigm that outputs representative opinions and corresponding rationales. |
| Outcome: | The proposed method is more useful than conventional summarizations. |
DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages (2022.emnlp-main)
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| Challenge: | Recent advances in neural language modeling and multilingual training have prompted widespread adoption of machine translation (MT) technologies across an unprecedented range of world languages. |
| Approach: | They propose to use a dataset to assess the impact of two state-of-the-art NMT systems, Google Translate and the multilingual mBART-50 model, on translation productivity. |
| Outcome: | The proposed model is faster than translation from scratch, but the magnitude of productivity gains varies widely across systems and languages. |
A Deep Metric Learning Method for Biomedical Passage Retrieval (2020.coling-main)
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| Challenge: | Existing methods for passage retrieval are based on metric learning . the proposed approach is particularly well suited for domain-specific passage retrievals where it is very important to take into account different sources of information. |
| Approach: | They propose a method that learns a metric for questions and passages based on their internal semantic interactions. |
| Outcome: | The proposed method outperforms triplet loss and state-of-the-art methods in a biomedical passage retrieval task and outperformed triplet losses by 10% and 26%. |
Biomedical Concept Relatedness – A large EHR-based benchmark (2020.coling-main)
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| Challenge: | Existing biomedical concept relatedness datasets are notoriously small and consist of hand-picked concept pairs. |
| Approach: | They propose to use a concept relatedness benchmark to test the suitability of AI in healthcare . they find that it is six times larger than existing concepts relatedness datasets . |
| Outcome: | The proposed benchmark is six times larger than existing biomedical concept relatedness datasets and is relevant for the application of interest. |
Towards a Gold Standard for Evaluating Danish Word Embeddings (2020.lrec-1)
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| Challenge: | Existing word embedding models resemble semantic similarity solely by distribution, but there seems to be a need for future judgments to measure similarity in full context and along more than a single spectrum. |
| Approach: | They propose a model-agnostic similarity goal standard for evaluating Danish word embeddings based on human judgments made by 42 native speakers of Danish. |
| Outcome: | The goal standard is applied to evaluate Danish word embeddings on 42 native speakers of Danish. |
SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages (L18-1)
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| Challenge: | SemR-11 is a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages. |
| Approach: | This paper describes a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages. |
| Outcome: | The dataset is a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages. |
Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings (2021.emnlp-main)
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| Challenge: | Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddable sentences labelled as semantically similar by annotators. |
| Approach: | They propose a language-independent approach to build large datasets of pairs of informal texts weakly similar, without manual human effort, exploiting Twitter’s powerful signals of relatedness: replies and quotes of tweets. |
| Outcome: | The proposed model learns classical Semantic Textual Similarity, and excels on tasks where pairs of sentences are not exact paraphrases. |
Dynamic Knowledge Integration for Evidence-Driven Counter-Argument Generation with Large Language Models (2025.findings-acl)
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| Challenge: | Argumentation in natural language processing (NLP) is becoming an indispensable tool in many application domains such as public policy, law, medicine, and education. |
| Approach: | They propose a reconstructed dataset of argument and counter-argument pairs . they propose integrating dynamic external knowledge from the web to improve counter-arguments . |
| Outcome: | The proposed method shows stronger correlation with human judgments compared to reference-based metrics. |