arXiv:1809.06152
공식 코드 · Papers with Code
Categorizing Comparative Sentences
원문 초록
We tackle the tasks of automatically identifying comparative sentences and categorizing the intended preference (e.g., "Python has better NLP libraries than MATLAB" => (Python, better, MATLAB). To this end, we manually annotate 7,199 sentences for 217 distinct target item pairs from several domains (27% of the sentences contain an oriented comparison in the sense of "better" or "worse"). A gradient boosting model based on pre-trained sentence embeddings reaches an F1 score of 85% in our experimental evaluation. The model can be used to extract comparative sentences for pro/con argumentation in comparative / argument search engines or debating technologies.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering
Sentence Embeddings as an intermediate target in end-to-end summarisation
JTCSE: Joint Tensor-Modulus Constraints and Cross-Attention for Unsupervised Contrastive Learning of Sentence Embeddings
Information Leakage of Sentence Embeddings via Generative Embedding Inversion Attacks
Domain Adaptation for Japanese Sentence Embeddings with Contrastive Learning based on Synthetic Sentence Generation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.