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This is\nobtained by a simple method in the context of solving the paraphrase generation\ntask. If we use a sequential encoder-decoder model for generating paraphrase,\nwe would like the generated paraphrase to be semantically close to the original\nsentence. One way to ensure this is by adding constraints for true paraphrase\nembeddings to be close and unrelated paraphrase candidate sentence embeddings\nto be far. This is ensured by using a sequential pair-wise discriminator that\nshares weights with the encoder that is trained with a suitable loss function.\nOur loss function penalizes paraphrase sentence embedding distances from being\ntoo large. This loss is used in combination with a sequential encoder-decoder\nnetwork. We also validated our method by evaluating the obtained embeddings for\na sentiment analysis task. The proposed method results in semantic embeddings\nand outperforms the state-of-the-art on the paraphrase generation and sentiment\nanalysis task on standard datasets. These results are also shown to be\nstatistically significant.","url_abs":"http://arxiv.org/abs/1806.00807v5","url_pdf":"http://arxiv.org/pdf/1806.00807v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-semantic-sentence-embeddings-using-1","repo_url":"https://github.com/vinodkkurmi/PQG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"learning-semantic-sentence-embeddings-using-1","repo_url":"https://github.com/dev-chauhan/PQG-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"paraphrase-generation","task_name":"Paraphrase Generation"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embedding","task_name":"Sentence Embedding"},{"task_slug":"sentence-embeddings","task_name":"Sentence Embeddings"},{"task_slug":"sentence-embedding-1","task_name":"Sentence-Embedding"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.00807","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.00807"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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