{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fine-grained-analysis-of-sentence-embeddings","title":"Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks","arxiv_id":"1608.04207","date":"2016-08-15","proceeding":null,"authors":["Yossi Adi","Einat Kermany","Yonatan Belinkov","Ofer Lavi","Yoav Goldberg"],"abstract":"There is a lot of research interest in encoding variable length sentences\ninto fixed length vectors, in a way that preserves the sentence meanings. Two\ncommon methods include representations based on averaging word vectors, and\nrepresentations based on the hidden states of recurrent neural networks such as\nLSTMs. The sentence vectors are used as features for subsequent machine\nlearning tasks or for pre-training in the context of deep learning. However,\nnot much is known about the properties that are encoded in these sentence\nrepresentations and about the language information they capture. We propose a\nframework that facilitates better understanding of the encoded representations.\nWe define prediction tasks around isolated aspects of sentence structure\n(namely sentence length, word content, and word order), and score\nrepresentations by the ability to train a classifier to solve each prediction\ntask when using the representation as input. We demonstrate the potential\ncontribution of the approach by analyzing different sentence representation\nmechanisms. The analysis sheds light on the relative strengths of different\nsentence embedding methods with respect to these low level prediction tasks,\nand on the effect of the encoded vector's dimensionality on the resulting\nrepresentations.","url_abs":"http://arxiv.org/abs/1608.04207v3","url_pdf":"http://arxiv.org/pdf/1608.04207v3.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":"fine-grained-analysis-of-sentence-embeddings","repo_url":"https://github.com/facebookresearch/InferSent","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"fine-grained-analysis-of-sentence-embeddings","repo_url":"https://github.com/facebookresearch/SentEval","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"fine-grained-analysis-of-sentence-embeddings","repo_url":"https://github.com/sid7954/NLP-Toolkit","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1608.04207","atlas_url":"https://app.syntology.ai/?focus=1608.04207","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}