| Challenge: | Existing approaches to semantic parsing work by training individual models for each available parallel dataset of text-meaning pairs. |
| Approach: | They propose a polyglot semantic translation approach that trains on multiple datasets and natural languages to learn parsing models. |
| Outcome: | The proposed model can be used for parsing a wide variety of natural languages and output languages, and achieves state-of-the-art performance on the above datasets. |
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Polyglot Semantic Role Labeling (P18-2)
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| Challenge: | Existing approaches to multilingual semantic dependency parsing treat languages independently, without exploiting similarities between semantic structures across languages. |
| Approach: | They propose to combine resources from different languages in a CoNLL 2009 shared task to build a single polyglot semantic dependency parser. |
| Outcome: | The proposed model outperforms monolingual training on a CoNLL 2009 dataset with training data from multiple languages and representations using multilingual word vectors. |
Neural Semantic Parsing (P18-5)
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| Challenge: | Semantic parsing is the study of translating natural language utterances into machine-executable programs. |
| Approach: | They will describe the various approaches researchers have taken to translate natural language into a formal language . they will also discuss why much recent work has chosen to use standard programming languages instead of more linguistically-motivated representations. |
| Outcome: | This paper will describe the various approaches researchers have taken to translate natural language into a formal language. |
Accurate polyglot semantic parsing with DAG grammars (2020.findings-emnlp)
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| Challenge: | Semantic parsers treat graphs as strings or trees, but there is no guarantee that the output is a well-formed graph. |
| Approach: | They propose a graph-aware sequence model that utilizes a DAG grammar to guide graph generation. |
| Outcome: | The proposed model outperforms string-based and DAG-grammar models by a large margin and can guarantee the well-formed graphs. |
SGL: Speaking the Graph Languages of Semantic Parsing via Multilingual Translation (2021.naacl-main)
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| Challenge: | Graph-based semantic parsing is one of the most promising general-purpose meaning representations . owing to this heterogeneity, most research focused on solutions specific to a given formalism . |
| Approach: | They propose a multilingual neural machine translation framework for Graph-based semantic parsing . they propose Graph2seq architecture that trains with an MNMT objective . |
| Outcome: | The proposed framework outperforms all competitors on cross-lingual parsing tasks. |
Compositional Semantic Parsing across Graphbanks (P19-1)
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| Challenge: | Existing semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. |
| Approach: | They propose a compositional neural semantic parser which achieves competitive accuracies across graphbanks. |
| Outcome: | The proposed system achieves competitive accuracies across a variety of graphbanks. |
A Tour of Explicit Multilingual Semantics: Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing (2022.aacl-tutorials)
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| Challenge: | a recent advent of pretrained language models has sparked a revolution in NLP . but, there are still questions about whether current approaches capture explicit, symbolic meaning . this tutorial will review efforts to tackle three key open problems in lexical and sentence-level semantics . |
| Approach: | This tutorial reviews recent efforts to shed light on meaning in NLP . it will focus on three key open problems in lexical and sentence-level semantics . |
| Outcome: | This tutorial reviews recent efforts to shed light on meaning in NLP . it focuses on three key open problems in lexical and sentence-level semantics . |
Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup (2022.findings-emnlp)
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| Challenge: | Experimental results show that Rex can benefit from cross-lingual training and improve the effectiveness of semantic parsers. |
| Approach: | They propose a Representation Mixup Framework for effectively exploiting translations in the cross-lingual Text-to-SQL task. |
| Outcome: | The proposed framework can benefit from cross-lingual training and improve the effectiveness of semantic parsers, achieving state-of-the-art performance. |
Semantics as a Foreign Language (D18-1)
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| Challenge: | (2017): Syntactic grammars capture propositions, but graph-based representations aim to capture a wider notion of propositions. |
| Approach: | They propose a neural sequence-to-sequence framework which can recover syntactic linearizations by a sequence-based approach. |
| Outcome: | The proposed framework performs almost on-par with previous state-of-the-art approaches while requiring less parallel training annotations. |
Learning Cross-lingual Distributed Logical Representations for Semantic Parsing (P18-2)
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| Challenge: | Recent research efforts have looked into the problem of learning semantic parsers in a multilingual setup, but how to improve the performance of a monolingual semantic parsed system remains a research question that is under-explored. |
| Approach: | They propose to use data annotated in different languages to learn distributed representations of logical forms for improving a monolingual semantic parser. |
| Outcome: | The proposed method improves on the standard multilingual GeoQuery dataset. |
Graph-Based Decoding for Task Oriented Semantic Parsing (2021.findings-emnlp)
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| Challenge: | Existing paradigms for semantic parsing are sequence-to-sequence and AMR parsers. |
| Approach: | They propose to formulate parsing as a sequence-to-sequence task using graph-based decoding techniques developed for syntactic parsers. |
| Outcome: | The proposed approach is competitive with sequence decoders on the standard setting and offers significant improvements in data efficiency and data availability. |