scholarly journals Learning to Map Frequent Phrases to Sub-Structures of Meaning Representation for Neural Semantic Parsing

2020 ◽  
Vol 34 (05) ◽  
pp. 7546-7553
Author(s):  
Bo Chen ◽  
Xianpei Han ◽  
Ben He ◽  
Le Sun
Keyword(s):  
Natural Language ◽  
Performance Improvement ◽  
Search Space ◽  
Great Benefit ◽  
Semantic Parsing ◽  
Mismatch Problem ◽  
Weakly Supervised ◽  
Meaning Representation

Neural semantic parsers usually generate meaning representation tokens from natural language tokens via an encoder-decoder model. However, there is often a vocabulary-mismatch problem between natural language utterances and logical forms. That is, one word maps to several atomic logical tokens, which need to be handled as a whole, rather than individual logical tokens at multiple steps. In this paper, we propose that the vocabulary-mismatch problem can be effectively resolved by leveraging appropriate logical tokens. Specifically, we exploit macro actions, which are of the same granularity of words/phrases, and allow the model to learn mappings from frequent phrases to corresponding sub-structures of meaning representation. Furthermore, macro actions are compact, and therefore utilizing them can significantly reduce the search space, which brings a great benefit to weakly supervised semantic parsing. Experiments show that our method leads to substantial performance improvement on three benchmarks, in both supervised and weakly supervised settings.

scholarly journals A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing

2014 ◽  
Vol 2 ◽  
pp. 547-560 ◽  
Author(s):  
Andreas Vlachos ◽  
Stephen Clark
Keyword(s):  
Information System ◽  
Natural Language ◽  
Learning Algorithm ◽  
Imitation Learning ◽  
Semantic Parsing ◽  
Learning Framework ◽  
Test Sets ◽  
Context Dependent ◽  
Meaning Representation ◽  
Tourist Information

Semantic parsing is the task of translating natural language utterances into a machine-interpretable meaning representation. Most approaches to this task have been evaluated on a small number of existing corpora which assume that all utterances must be interpreted according to a database and typically ignore context. In this paper we present a new, publicly available corpus for context-dependent semantic parsing. The MRL used for the annotation was designed to support a portable, interactive tourist information system. We develop a semantic parser for this corpus by adapting the imitation learning algorithm DAgger without requiring alignment information during training. DAgger improves upon independently trained classifiers by 9.0 and 4.8 points in F-score on the development and test sets respectively.

scholarly journals Semantic Parsing of Ambiguous Input through Paraphrasing and Verification

2015 ◽  
Vol 3 ◽  
pp. 571-584
Author(s):  
Philip Arthur ◽  
Graham Neubig ◽  
Sakriani Sakti ◽  
Tomoki Toda ◽  
Satoshi Nakamura
Keyword(s):  
Natural Language ◽  
Language Model ◽  
Input String ◽  
New Method ◽  
Semantic Parsing ◽  
Search Queries ◽  
Input Sentence ◽  
Meaning Representation ◽  
Context Free ◽  
Do So

We propose a new method for semantic parsing of ambiguous and ungrammatical input, such as search queries. We do so by building on an existing semantic parsing framework that uses synchronous context free grammars (SCFG) to jointly model the input sentence and output meaning representation. We generalize this SCFG framework to allow not one, but multiple outputs. Using this formalism, we construct a grammar that takes an ambiguous input string and jointly maps it into both a meaning representation and a natural language paraphrase that is less ambiguous than the original input. This paraphrase can be used to disambiguate the meaning representation via verification using a language model that calculates the probability of each paraphrase.

scholarly journals Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering

2020 ◽  
Author(s):  
Tao Shen ◽  
Xiubo Geng ◽  
Guodong Long ◽  
Jing Jiang ◽  
Chengqi Zhang ◽  
...  
Keyword(s):  
Question Answering ◽  
Logical Form ◽  
Computation Time ◽  
Search Space ◽  
Training Data ◽  
Semantic Parsing ◽  
Weak Supervision ◽  
Knowledge Based ◽  
Model Training ◽  
Weakly Supervised

Many algorithms for Knowledge-Based Question Answering (KBQA) depend on semantic parsing, which translates a question to its logical form. When only weak supervision is provided, it is usually necessary to search valid logical forms for model training. However, a complex question typically involves a huge search space, which creates two main problems: 1) the solutions limited by computation time and memory usually reduce the success rate of the search, and 2) spurious logical forms in the search results degrade the quality of training data. These two problems lead to a poorly-trained semantic parsing model. In this work, we propose an effective search method for weakly supervised KBQA based on operator prediction for questions. With search space constrained by predicted operators, sufficient search paths can be explored, more valid logical forms can be derived, and operators possibly causing spurious logical forms can be avoided. As a result, a larger proportion of questions in a weakly supervised training set are equipped with logical forms, and fewer spurious logical forms are generated. Such high-quality training data directly contributes to a better semantic parsing model. Experimental results on one of the largest KBQA datasets (i.e., CSQA) verify the effectiveness of our approach and deliver a new state-of-the-art performance.

scholarly journals Break It Down: A Question Understanding Benchmark

2020 ◽  
Vol 8 ◽  
pp. 183-198
Author(s):  
Tomer Wolfson ◽  
Mor Geva ◽  
Ankit Gupta ◽  
Matt Gardner ◽  
Yoav Goldberg ◽  
...  
Keyword(s):  
Natural Language ◽  
Formal Language ◽  
Question Answering ◽  
Semantic Parsing ◽  
Open Domain ◽  
Meaning Representation

Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through natural language, that are necessary for answering a question. We develop a crowdsourcing pipeline, showing that quality QDMRs can be annotated at scale, and release the Break dataset, containing over 83K pairs of questions and their QDMRs. We demonstrate the utility of QDMR by showing that (a) it can be used to improve open-domain question answering on the HotpotQA dataset, (b) it can be deterministically converted to a pseudo-SQL formal language, which can alleviate annotation in semantic parsing applications. Last, we use Break to train a sequence-to-sequence model with copying that parses questions into QDMR structures, and show that it substantially outperforms several natural baselines.

scholarly journals Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions

2013 ◽  
Vol 1 ◽  
pp. 49-62 ◽  
Author(s):  
Yoav Artzi ◽  
Luke Zettlemoyer
Keyword(s):  
Natural Language ◽  
Supervised Learning ◽  
State Of The Art ◽  
Semantic Parsing ◽  
Weak Supervision ◽  
Instruction Sets ◽  
Strong Signal ◽  
Previous State ◽  
Strong Performance ◽  
Weakly Supervised

The context in which language is used provides a strong signal for learning to recover its meaning. In this paper, we show it can be used within a grounded CCG semantic parsing approach that learns a joint model of meaning and context for interpreting and executing natural language instructions, using various types of weak supervision. The joint nature provides crucial benefits by allowing situated cues, such as the set of visible objects, to directly influence learning. It also enables algorithms that learn while executing instructions, for example by trying to replicate human actions. Experiments on a benchmark navigational dataset demonstrate strong performance under differing forms of supervision, including correctly executing 60% more instruction sets relative to the previous state of the art.

scholarly journals Large-scale Semantic Parsing without Question-Answer Pairs

2014 ◽  
Vol 2 ◽  
pp. 377-392 ◽  
Author(s):  
Siva Reddy ◽  
Mirella Lapata ◽  
Mark Steedman
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Graph Matching ◽  
State Of The Art ◽  
The State ◽  
Semantic Parsing ◽  
Matching Problem ◽  
Weak Supervision ◽  
Benchmark Datasets

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.

scholarly journals RECPARSER: A Recursive Semantic Parsing Framework for Text-to-SQL Task

2020 ◽  
Author(s):  
Yu Zeng ◽  
Yan Gao ◽  
Jiaqi Guo ◽  
Bei Chen ◽  
Qian Liu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Search Space ◽  
Layer By Layer ◽  
Semantic Parsing ◽  
Query Generation ◽  
Sql Query ◽  
Generation Problem

Neural semantic parsers usually fail to parse long and complicated utterances into nested SQL queries, due to the large search space. In this paper, we propose a novel recursive semantic parsing framework called RECPARSER to generate the nested SQL query layer-by-layer. It decomposes the complicated nested SQL query generation problem into several progressive non-nested SQL query generation problems. Furthermore, we propose a novel Question Decomposer module to explicitly encourage RECPARSER to focus on different components of an utterance when predicting SQL queries of different layers. Experiments on the Spider dataset show that our approach is more effective compared to the previous works at predicting the nested SQL queries. In addition, we achieve an overall accuracy that is comparable with state-of-the-art approaches.

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