Context Dependent Semantic Parsing: A Survey (2020)

Abstract: Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations. Currently, most semantic parsing methods are not able to utilize the contextual information (e.g. dialogue and comments history), which has a great potential to boost the semantic parsing systems. To address this issue, context dependent semantic parsing has recently drawn a lot of attention. In this survey, we investigate progress on the methods for the context dependent semantic parsing, together with the current datasets and tasks. We then point out open problems and challenges for future research in this area.

## introduction semantic parsing is concerned with mapping natural language (nl) utterances into machine-readable structured meaning representations (mrs). these representations are in the formats of formal languages, e.g. prolog, sql, and python. a formal language is typically defined by means of a formal grammar, which consists of a set of rules. following the convention of the chosen formal language, mrs are also referred to as logical forms or programs. an mr is often executable in a (programming) environment to yield a result (e.g. results of sql queries) enabling automated reasoning @xcite . most research work on semantic parsing treats each nl utterance as an independent input, ignoring the text surrounding them @xcite , such as interaction histories in dialogues. the surrounding text varies significantly across different application scenarios. in a piece of free text, we refer to the surrounding text of a current utterance as its context. the context is different with respect to different utterances. in our sequel, we differentiate between context independent semantic parsing (cisp) and context dependent semantic parsing (cdsp) by whether a corresponding parser utilizes context information. a knowledge base or a database (on which a mr is executed for the purpose of question answering) can be considered as context as well @xcite . this type of context does not change with respect to the utterances. in this survey, we only consider the former kind of context which does vary with different utterances. d database about pets q 1 what are the different pet types? s1 select distinct pettype from pets q 2 for each of those, what is the maximum age? s2 select max(pet age), pettype from pets group by pettype q 3 what about the average age? s3 select avg(pet age), pettype from pets group by pettype the utilization of context in semantic parsing imposes both challenges and opportunities. as shown in this paper is organised as follows. we start with providing a brief and fundamental understanding of cisp in §2. we then present a comprehensive organization of the recent advances in cdsp in §3. we discuss current cisp tasks, datasets, and resources in §4. finally, we cover open research problems in §5, and conclude by providing a roadmap for future research in this area. ## background cisp aims to learn a mapping π θ : @xmath0, which translates an nl utterance @xmath1. an mr y can be executed in a programming environment (e.g. databases, knowledge graphs, etc.) to yield a result z, namely denotation. the structure of an mr takes a form of either a tree or graph, depending on its underlying formal language. the languages of mrs are categorized into three types of formalism : logic based (e.g. first order logic), graph based (e.g. amr @xcite ), and programming languages (e.g. java, python) @xcite . some semantic parsers explicitly apply a production grammar to yield mrs from utterances. such a grammar consists of a set of production rules, which define a list of candidate derivations for each nl utterance. each derivation deterministically produces a grammatically valid mr. ## context dependent semantic parsing context dependent semantic parsing involves modelling of context in the parsing process. for each current nl utterance, we define its context as the information beyond this utterance. with this definition, there are two types of context for semantic parsing, local context and global context. the local context for an utterance is the text and multimedia content surrounding it, which is meaningful only for this utterance. in plain texts, the concept of local context is also quite close to discourse, which is defined as a group of collocated, structured, coherent sentences @xcite . in contrast, its global context is the information accessible to more than one utterance, including databases and external text corpora, images or class environment @xcite . the content of local context varies for each nl utterance while the global context is always static. the work in our survey is only concerned with local context. therefore, we always refer to "local context" as "context" in the following sections. context provides additional information to resolve ambiguity and vagueness in current utterances. for semantic parsing, one type of ambiguity is caused by references in current utterances, which need to be resolved to previously mentioned objects and relations. references may include explicit or implicit lexical triggers, such as those in "for each of those, ..." in our introductory example ( formally, a context dependent parser takes both an input utterance x i and its context c i , where c i could include a broad range of multimedia content. and we consider a group of inter-related utterances with the union set of their context as one interaction, @xmath2, c), where @xmath3, ..., x i , ..., x t ] and @xmath4. currently, most cdsp work focus on the research problems of context c i regarding the history utterances, mrs, denotations. such a parser learns a mapping from a current utterance x i to an mr y i by π θ (x i , c i ). ## challenges and future directions cdsp distinct from cisp by context modelling and utilization of context information in the parsing process to complete missing information in mrs. despite significant progress in recent years, there are still multiple directions worth pursuing. yu et al. (2018) and liu et al. (2020) analyzed the influence of different types of contextual information on cdsp methods. despite some empirical results, it still lacks of a thorough understanding of pros and cons of each type of context in relation to the parsing task. for example, in which cases should parsers extract information from mrs in context instead of utterances? apart from ellipsis and coreference resolution, are there other linguistically motivated problems in context the current parsers have not addressed yet?