Context Dependent Semantic Parsing: A Survey

Li, Zhuang; Qu, Lizhen; Haffari, Gholamreza

doi:10.18653/v1/2020.coling-main.226

Cited by 13 publications

(7 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The user questions expressed in natural language are converted into SQL queries which are then executed on our Maritime DeepDive Knowledge Graph [34] to retrieve answers to the questions. The recent surveys [13], [16], [43] cover comprehensive reviews about recent semantic parsing studies. Of particular relevance to this work are BootStrapping Semantic Parsers.…”

Section: Background a Semantic Parsingmentioning

confidence: 99%

“…Specifically, by designing and constructing an appropriate input sequence of words (called a prompt), one is able to induce the model to produce the desired output sequence (i.e., a paraphrased utterance in this context)without changing its parameters through fine-tuning at all. In particular, in the low-data regime, empirical analysis shows that, either for manually picking hand-crafted prompts [21] or automatically building auto-generated prompts [8], [16] taking prompts for tuning models is surprisingly effective for the knowledge stimulation and model adaptation of pre-trained language model. However, none of these methods reports the use of a few-shot pre-trained language model to directly generate few-shot and zero-shot paraphrased utterances.…”

Section: Paraphrase Generationmentioning

confidence: 99%

See 1 more Smart Citation

Paraphrasing Techniques for Maritime QA system

Shiri

Yue

et al. 2022

2022 25th International Conference on Information Fusion (FUSION)

View full text Add to dashboard Cite

There has been an increasing interest in incorporating Artificial Intelligence (AI) into Defence and military systems to complement and augment human intelligence and capabilities. However, much work still needs to be done toward achieving an effective human-machine partnership. This work is aimed at enhancing human-machine communications by developing a capability for automatically translating human natural language into a machine-understandable language (e.g., SQL queries). Techniques toward achieving this goal typically involve building a semantic parser trained on a very large amount of high-quality manually-annotated data. However, in many real-world Defence scenarios, it is not feasible to obtain such a large amount of training data. To the best of our knowledge, there are few works trying to explore the possibility of training a semantic parser with limited manually-paraphrased data, in other words, zero-shot. In this paper, we investigate how to exploit paraphrasing methods for the automated generation of large-scale training datasets (in the form of paraphrased utterances and their corresponding logical forms in SQL format) and present our experimental results using real-world data in the maritime domain.

show abstract

Section: Background a Semantic Parsingmentioning

confidence: 99%

Section: Paraphrase Generationmentioning

confidence: 99%

Paraphrasing Techniques for Maritime QA system

Shiri

Yue

et al. 2022

2022 25th International Conference on Information Fusion (FUSION)

View full text Add to dashboard Cite

show abstract

“…Dataset XDTS is a sub-task of contextdependent semantic parsing (CDSP) (Suhr et al, 2018;Guo et al, 2019a;Li et al, 2020). Many datasets have been constructed for CDSP.…”

Section: Related Workmentioning

confidence: 99%

Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL

Guo¹,

Si²,

Wu³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

View full text Add to dashboard Cite

The cross-database context-dependent Text-to-SQL (XDTS) problem has attracted considerable attention in recent years due to its wide range of potential applications. However, we identify two biases in existing datasets for XDTS: (1) a high proportion of contextindependent questions and (2) a high proportion of easy SQL queries. These biases conceal the major challenges in XDTS to some extent. In this work, we present CHASE, a large-scale and pragmatic Chinese dataset for XDTS. It consists of 5,459 coherent question sequences (17,940 questions with their SQL queries annotated) over 280 databases, in which only 35% of questions are contextindependent, and 28% of SQL queries are easy. We experiment on CHASE with three state-ofthe-art XDTS approaches. The best approach only achieves an exact match accuracy of 40% over all questions and 16% over all question sequences, indicating that CHASE highlights the challenging problems of XDTS. We believe that CHASE can provide fertile soil for addressing the problems. * Work done during an internship at Microsoft Research. 哪所大学培养了最多 MVP 球员？ Which university has the most MVP players? SELECT T2.毕业院校 FROM MVP记录 AS T1 JOIN 球员 AS T2 ON T1.球员id = T2.球员id GROUP BY T2.毕业院校 ORDER BY COUNT(DISTINCT T2.球员id) DESC LIMIT 1; SELECT T2.college FROM MVP_record AS T1 JOIN player AS T2 ON T1.player_id = T2.player_id GROUP BY T2.college ORDER BY COUNT(DISTINCT T2.player_id) DESC LIMIT 1; 状元呢？ How about the first overall pick? 居然还是肯塔基！杜克也很出名啊，它培养了多少呢？ Still Kentucky! Duke is also very famous! How many does it have? SELECT 毕业院校 FROM 球员 WHERE 是否状元 = "是" GROUP BY 毕业院校 ORDER BY COUNT(*) DESC LIMIT 1; SELECT college FROM player WHERE is_first_pick = "yes" GROUP BY college ORDER BY COUNT(*) DESC LIMIT 1; SELECT COUNT(*) FROM 球员 WHERE 是否状元 = "是" AND 毕业院校 LIKE "%杜克%";

show abstract

“…Context, in this scenario, is broadly understood as information extracted from the set of words occurring around a given word token (Smith, 2020 ; Xia et al, 2020 ). The term context is also used in NLP to refer to information that can be extracted from sentences around the one being analyzed for a given task, such as Natural Language Generation (NLG) for question answering and dialogue systems (Zhou et al, 2016 ) or context-aware semantic parsing (Li et al, 2020b ). Also, identifying and modeling commonsense knowledge is a key aspect of tasks involving Natural Language Understanding (NLU) and Inference (NLI) (LoBue and Yates, 2011 ; Sap et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Representing Context in FrameNet: A Multidimensional, Multimodal Approach

et al. 2022

View full text Add to dashboard Cite

Frame Semantics includes context as a central aspect of the theory. Frames themselves can be regarded as a representation of the immediate context against which meaning is to be construed. Moreover, the notion of frame invocation includes context as one possible source of information comprehenders use to construe meaning. As the original implementation of Frame Semantics, Berkeley FrameNet is capable of providing computational representations of some aspects of context, but not all of them. In this article, we present FrameNet Brasil: a framenet enriched with qualia relations and capable of taking other semiotic modes as input data, namely pictures and videos. We claim that such an enriched model is capable of addressing other types of contextual information in a framenet, namely sentence-level cotext and commonsense knowledge. We demonstrate how the FrameNet Brasil software infrastructure addresses contextual information in both database construction and corpora annotation. We present the guidelines for the construction of two multimodal datasets whose annotations represent contextual information and also report on two experiments: (i) the identification of frame-evoking lexical units in sentences and (ii) a methodology for domain adaptation in Neural Machine Translation that leverages frames and qualia for representing sentence-level context. Experimental results emphasize the importance of computationally representing contextual information in a principled structured fashion as opposed to trying to derive it from the manipulation of linguistic form alone.

show abstract

Context Dependent Semantic Parsing: A Survey

Cited by 13 publications

References 42 publications

Paraphrasing Techniques for Maritime QA system

Paraphrasing Techniques for Maritime QA system

Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL

Representing Context in FrameNet: A Multidimensional, Multimodal Approach

Contact Info

Product

Resources

About