Challenge: Context-dependent semantic parsing has proved to be an important but challenging task.
Approach: They propose to perform follow-up query analysis to restate context-dependent queries with contextual information.
Outcome: The proposed approach outperforms the state-of-the-art by nearly 8% on the FollowUp dataset . the extensibility of STAR on the SQA dataset is also promising .

Similar Papers

Context Dependent Semantic Parsing: A Survey (2020.coling-main)

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Challenge: Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations.
Approach: They propose to use contextual information to translate natural language utterances into machine-readable meaning representations.
Outcome: The proposed methods do not utilize contextual information, which could boost the semantic parsing systems.
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing (2022.findings-emnlp)

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Challenge: Extensive experiments show that STAR outperforms previous pre-training methods and ranks first on the leaderboard . text-to-SQL parsing aims to translate natural language (NL) questions into executable SQL queries .
Approach: They propose a SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing . they propose two objectives that explore context-dependence of NL utterances and SQL queries .
Outcome: The proposed framework outperforms existing methods on two downstream benchmarks and ranks first on the leaderboard.
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks (P19-1)

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Challenge: Existing approaches to semantic dependency parsing are graph-based and transition-based.
Approach: They propose a second-order semantic dependency parser which takes relationships between two or more edges into account.
Outcome: The proposed algorithm outperforms existing approaches to parsing on graph-based approaches.
A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing (2020.coling-main)

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Challenge: Existing methods for text-to-SQL semantic parsing require strict structured prediction due to its application scenario where the output SQL will be sent to an executor program directly.
Approach: They propose to use schema linking and structural linking to link NL to the database schema.
Outcome: The proposed method shows significant gains on the Spider dataset.
Top-down Discourse Parsing via Sequence Labelling (2021.eacl-main)

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Challenge: Discourse analysis is a systematic way to understand how texts are segmented hierarchically into discourse units.
Approach: They propose a top-down approach to discourse parsing that is conceptually simpler than its predecessors.
Outcome: The proposed model eliminates the decoder and reduces the search space for splitting points.
TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction (2024.findings-acl)

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Challenge: Recent studies suggest that event extraction evaluations may not accurately reflect the true performance.
Approach: They propose a standardized, fair, and reproducible benchmark for event extraction . they use standardized scripts and splits for 16 datasets spanning eight domains .
Outcome: The proposed benchmarks show that they struggle to achieve satisfactory performance.
RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction (2023.acl-long)

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Challenge: Existing methods for zero-shot relation extraction lack explicit modeling of matching pattern . et al. (2018) show that our method achieves higher matching accuracy and faster inference speed .
Approach: They propose a fine-grained semantic matching method tailored for zero-shot relation extraction . they decompose sentence-level similarity score into entity matching score and context matching score .
Outcome: The proposed method achieves higher matching accuracy and faster inference speed than state-of-the-art methods.
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions (D19-1)

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Challenge: Generating SQL queries from user utterances is an important task to help end users acquire information from databases.
Approach: They propose a context-dependent text-to-SQL generation task that edits previous queries . they use an utterance-table encoder and a table-aware decoder to incorporate context .
Outcome: The proposed model is flexible to change individual tokens and robust to error propagation.
HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing (2022.findings-acl)

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Challenge: Recent studies focus on context-dependent text-to-SQL task but fail to exploit both . et al., 2019; xu e. al.; yu y., 2021) focus on the context-independent text to SQL task .
Approach: They propose a history information enhanced text-to-SQL model to exploit context dependence information from history utterances and the last predicted SQL query.
Outcome: The proposed model improves performance on two context-dependent text-to-SQL benchmarks.
Cross-lingual Decompositional Semantic Parsing (D18-1)

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Challenge: Renewed interest in semantic analysis has led to a surge of proposed new frameworks . many of these efforts are limited to the analysis of English, but with a number of exceptions e.g., recent efforts in Minimal Recursion Semantics (MRS) and multilingual FrameNet annotation and parsing.
Approach: They propose a cross-lingual decompositional semantic analysis task based on a target language . they propose 'end-to-end' model with an annotating mechanism that supports intra-sentential coreference .
Outcome: The proposed model outperforms baselines by at least 1.75 F1 score on an evaluation dataset.

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