Learning Thematic Similarity Metric from Article Sections Using Triplet Networks (P18-2)
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| Challenge: | In this paper, we use Wikipedia articles to learn thematic similarity metric between sentences. |
| Approach: | They propose to leverage the partition of articles into sections to learn thematic similarity metric between sentences. |
| Outcome: | The proposed model outperforms state-of-the-art embeddings on the task of thematic clustering of sentences. |
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| Challenge: | Existing word embeddings combine complementary strengths of their components to achieve unsupervised semantic similarity (STS). |
| Approach: | They propose to ensemble pre-trained sentence encoders into sentence meta-embeddings to achieve unsupervised Semantic Textual Similarity (STS) they adapt dimensionality reduction, generalized Canonical Correlation Analysis and cross-view auto-encoders to their work. |
| Outcome: | The proposed method achieves 3.7% to 6.4% Pearson’s r over single-source word embeddings on the STS Benchmark and on the StS12-STS16 datasets. |
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. |
Exploring Semantic Properties of Sentence Embeddings (P18-2)
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| Challenge: | Neural vector representations are ubiquitous throughout all subfields of natural language processing. |
| Approach: | They propose a framework that generates triplets of sentences to explore how changes in the syntactic structure or semantics of a given sentence affect their similarity. |
| Outcome: | The proposed framework generates triplets of sentences to explore how changes in the syntactic structure or semantics of a given sentence affect the similarities obtained between their embeddings. |
Semantic Alignment with Calibrated Similarity for Multilingual Sentence Embedding (2021.findings-emnlp)
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| Challenge: | Existing methods for learning semantic similarity between two English sentences have focused on one sub-task and therefore showed biased performance. |
| Approach: | They propose a method to learn semantic similarity between two English sentences using siamese networks. |
| Outcome: | The proposed method improves on both sub-tasks and predicts similarity scores in 14 languages. |
Going Beyond Sentence Embeddings: A Token-Level Matching Algorithm for Calculating Semantic Textual Similarity (2023.acl-short)
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| Challenge: | Semantic Textual Similarity (STS) measures the degree to which the underlying semantics of paired sentences are equivalent. |
| Approach: | They propose a token-level matching inference algorithm which can be applied on top of any language model to improve its performance on STS task. |
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Making Better Use of Training Corpus: Retrieval-based Aspect Sentiment Triplet Extraction via Label Interpolation (2023.findings-acl)
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| Challenge: | Existing methods to extract sentimental triplets are infeasible and counterproductive . aspect Sentiment Triplets Extraction (ASTE) task is an emerging sub-task of Aspect-based Sentimence Analysis . |
| Approach: | They propose a retrieval-based approach to the Aspect Sentiment Triplet Extraction task . they retrieve semantic similar triplets from the training corpus and interpolate their label information . |
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Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts (P19-1)
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| Challenge: | Existing automatic metrics for evaluating text are expensive and time-consuming. |
| Approach: | They propose automatic metrics that evaluate text in a continuous space using word and sentence embeddings. |
| Outcome: | The proposed method outperforms ROUGE on machine-generated summaries and human-authored essays on human-generated texts. |
From Semantics to Style: A Cross-Dataset Comparative Framework for Sentence Similarity Predictions (2026.findings-eacl)
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| Challenge: | Existing frameworks for analyzing text embedding models are limited. |
| Approach: | They propose a framework that uses lightweight poolers to analyze STS, PI, and Triplet datasets. |
| Outcome: | The proposed framework shows that the model captures semantic differences between sentences and is consistent across datasets. |
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (D19-1)
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| Challenge: | Existing methods for finding similar sentences require multiple inferences . a modern GPU requires 65 hours to find the most similar pair in 10,000 sentences . |
| Approach: | They propose a modification of the pretrained BERT network that uses siamese and triplet networks to derive semantically meaningful sentence embeddings. |
| Outcome: | The proposed method outperforms existing methods on sentence-pair regression tasks. |
Sentence Similarity Based on Contexts (2022.tacl-1)
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| Challenge: | Existing methods to measure sentence similarity face limited dataset size and training-test gap . existing methods lack large-scale labeled datasets with labeles that are labor-intensive and expensive . |
| Approach: | They propose a framework that measures sentence similarity by comparing probabilities of generating two sentences given the same context. |
| Outcome: | The proposed framework achieves significant performance boosts over baselines under supervised and unsupervised settings. |