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This paper introduces a la carte\nembedding, a simple and general alternative to the usual word2vec-based\napproaches for building such representations that is based upon recent\ntheoretical results for GloVe-like embeddings. Our method relies mainly on a\nlinear transformation that is efficiently learnable using pretrained word\nvectors and linear regression. This transform is applicable on the fly in the\nfuture when a new text feature or rare word is encountered, even if only a\nsingle usage example is available. We introduce a new dataset showing how the a\nla carte method requires fewer examples of words in context to learn\nhigh-quality embeddings and we obtain state-of-the-art results on a nonce task\nand some unsupervised document classification tasks.","url_abs":"http://arxiv.org/abs/1805.05388v1","url_pdf":"http://arxiv.org/pdf/1805.05388v1.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":"a-la-carte-embedding-cheap-but-effective","repo_url":"https://github.com/NLPrinceton/ALaCarte","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"document-classification","task_name":"Document Classification"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-cr","task":"Sentiment Analysis","dataset":"CR","model":"byte mLSTM7","rank_in_archive_order":5,"of":9,"metrics":{"Accuracy":"90.6"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-mpqa","task":"Sentiment Analysis","dataset":"MPQA","model":"byte mLSTM7","rank_in_archive_order":3,"of":4,"metrics":{"Accuracy":"88.8"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-mr","task":"Sentiment Analysis","dataset":"MR","model":"byte mLSTM7","rank_in_archive_order":4,"of":19,"metrics":{"Accuracy":"86.8"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-sst-2-binary","task":"Sentiment Analysis","dataset":"SST-2 Binary classification","model":"byte mLSTM7","rank_in_archive_order":49,"of":87,"metrics":{"Accuracy":"91.7"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-sst-5-fine-grained","task":"Sentiment Analysis","dataset":"SST-5 Fine-grained classification","model":"byte mLSTM7","rank_in_archive_order":9,"of":31,"metrics":{"Accuracy":"54.6"},"uses_additional_data":false},{"leaderboard":"/sota/subjectivity-analysis-on-subj","task":"Subjectivity Analysis","dataset":"SUBJ","model":"byte mLSTM7","rank_in_archive_order":8,"of":19,"metrics":{"Accuracy":"94.7"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-imdb","task":"Text Classification","dataset":"IMDb","model":"byte mLSTM7","rank_in_archive_order":4,"of":13,"metrics":{},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-trec-6","task":"Text Classification","dataset":"TREC-6","model":"byte mLSTM7","rank_in_archive_order":18,"of":19,"metrics":{"Error":"9.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.05388","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.05388"}},"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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