홈 › Papers

From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding

2021-06-11 · ACL 2021 5 · Shan Wu, Bo Chen, Chunlei Xin, Xianpei Han, Le Sun, Weipeng Zhang, Jiansong Chen, Fan Yang, Xunliang Cai

Semantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing method - Synchronous Semantic Decoding (SSD), which can simultaneously resolve the semantic gap and the structure gap by jointly leveraging paraphrasing and grammar constrained decoding. Specifically, we reformulate semantic parsing as a constrained paraphrasing problem: given an utterance, our model synchronously generates its canonical utterance and meaning representation. During synchronous decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly. Experimental results show that SSD is a promising approach and can achieve competitive unsupervised semantic parsing performance on multiple datasets.

📄 PDF Abstract BibTeX arXiv:2106.06228

Code (0)

등록된 구현이 없습니다.

Tasks

FormSemantic Parsing Unsupervised semantic parsing

Methods 이 논문이 사용한 방법론

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

Non Maximum Suppression Non Maximum Suppression is a computer vision method that selects a single entity out of many overlapping entities (for example bounding boxes in object detection). The…

1x1 Convolution A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction,…

SSD SSD is a single-stage object detection method that discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per…

Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing

2020-05-27 · ACL 2020 6 · Ruisheng Cao, Su Zhu, Chenyu Yang, Chen Liu 외

One daunting problem for semantic parsing is the scarcity of annotation. Aiming to reduce nontrivial human labor, we propose a two-stage semantic parsing framework, where the first stage utilizes an unsupervised paraphra…

Semantic Parsing Vocal Bursts Valence Prediction

Cross-domain Semantic Parsing via Paraphrasing

2017-04-20 · EMNLP 2017 9 · Yu Su, Xifeng Yan

Existing studies on semantic parsing mainly focus on the in-domain setting. We formulate cross-domain semantic parsing as a domain adaptation problem: train a semantic parser on some source domains and then adapt it to t…

Diversity Domain Adaptation Semantic Parsing Word Embeddings

Semantic Parsing via Paraphrasing

2014-06-01 · ACL 2014 6 · Jonathan Berant, Percy Liang

Question Answering Semantic Parsing

Training Naturalized Semantic Parsers with Very Little Data

2022-04-29 · Subendhu Rongali, Konstantine Arkoudas, Melanie Rubino, Wael Hamza

Semantic parsing is an important NLP problem, particularly for voice assistants such as Alexa and Google Assistant. State-of-the-art (SOTA) semantic parsers are seq2seq architectures based on large language models that h…

Semantic Parsing

Few-Shot Semantic Parsing with Language Models Trained On Code

2022-01-16 · ACL ARR January 2022 1 · Anonymous

Large language models can perform semantic parsing with little training data, when prompted with in-context examples. It has been shown that this can be improved by formulating the problem as paraphrasing into canonical …