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Due to the diversity of logical forms in different domains, this\nproblem presents unique and intriguing challenges. By converting logical forms\ninto canonical utterances in natural language, we reduce semantic parsing to\nparaphrasing, and develop an attentive sequence-to-sequence paraphrase model\nthat is general and flexible to adapt to different domains. We discover two\nproblems, small micro variance and large macro variance, of pre-trained word\nembeddings that hinder their direct use in neural networks, and propose\nstandardization techniques as a remedy. On the popular Overnight dataset, which\ncontains eight domains, we show that both cross-domain training and\nstandardized pre-trained word embeddings can bring significant improvement.","url_abs":"http://arxiv.org/abs/1704.05974v2","url_pdf":"http://arxiv.org/pdf/1704.05974v2.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":"cross-domain-semantic-parsing-via","repo_url":"https://github.com/ysu1989/CrossSemparse","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.05974","atlas_url":"https://app.syntology.ai/?focus=1704.05974","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.05974"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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