| Challenge: | Semantic parsing aims at translating natural language (NL) utterances onto machine-interpretable programs. |
| Approach: | They propose to encourage a parser to generate executable programs for unlabeled NL utterances. |
| Outcome: | The proposed training objectives outperform conventional methods on Overnight and GeoQuery. |
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Practical Semantic Parsing for Spoken Language Understanding (N19-2)
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| Challenge: | Existing systems that can handle a user's utterance are unable to handle Q&A or SLU. |
| Approach: | They build a transfer learning framework for executable semantic parsing . they show it is effective for Q&A and for spoken language understanding . |
| Outcome: | The proposed framework is effective for Q&A and Spoken Language Understanding . it can be learned by exploiting data on other domains, the authors show . |
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. |
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs (D19-1)
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| Challenge: | Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations. |
| Approach: | They propose to instill an inductive bias in the parser to help it distinguish between spurious and correct programs. |
| Outcome: | The proposed model is highly tractable on WikiTableQuestions and WikiSQL datasets. |
Weakly Supervised Semantic Parsing with Execution-based Spurious Program Filtering (2023.emnlp-main)
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| Challenge: | Existing methods to train a semantic parser from weak supervision focus on exploiting similarities between examples based on domain-specific knowledge. |
| Approach: | They propose a domain-agnostic filtering mechanism based on program execution results to identify and filter out programs with significantly different semantics from the other programs. |
| Outcome: | The proposed method improves the performance of existing weakly-supervised parsers by incorporating a majority vote on the program search results. |
On The Ingredients of an Effective Zero-shot Semantic Parser (2022.acl-long)
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| Challenge: | Recent studies have performed zero-shot learning by synthesizing training examples of canonical utterances and programs from a grammar, and further paraphrasing these utterrances to improve linguistic diversity. |
| Approach: | They propose to bridge gaps between canonical and real-world user-issued examples by using stronger paraphrasers and improved grammars. |
| Outcome: | The proposed model achieves strong performance on two semantic parsing benchmarks with zero labeled data. |
Online Semantic Parsing for Latency Reduction in Task-Oriented Dialogue (2022.acl-long)
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| Challenge: | Standard conversational semantic parsing maps a user's intent into an executable program, but execution is slow when expensive function calls are included. |
| Approach: | They propose a task of online semantic parsing to predict and execute function calls while the user is still speaking. |
| Outcome: | The proposed approach reduces latency with good parsing quality and execution cost. |
Grounding language acquisition by training semantic parsers using captioned videos (D18-1)
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| Challenge: | a new method for parsing sentences using captioned videos is being developed . we use video clips to ground the semantics of language, but without annotations . |
| Approach: | They develop a semantic parser that is trained in a grounded setting using captioned videos . they use a corpus of sentences paired with videos without other annotations to train it . |
| Outcome: | The proposed parser recovers the meaning of English sentences despite no annotations . learning a grounded semantic parsers can expand the range of data that parseurs can be trained on . |
Enforcing Consistency in Weakly Supervised Semantic Parsing (2021.acl-short)
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| Challenge: | Existing methods for training semantic parsers from only (utterance, denotation) supervision are challenging. |
| Approach: | They propose to use consistency between output programs for related inputs to reduce the impact of spurious programs. |
| Outcome: | The proposed formalisms improve model performance even without consistency-based training. |
Zero-Shot Semantic Parsing for Instructions (P19-1)
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| Challenge: | Recent years have seen an increasing number of applications that have a natural language interface, such as chatbots or "intelligent personal assistants" |
| Approach: | They propose a new training algorithm that trains a semantic parser on examples from a set of source domains and augment it with features and a logical form candidate filtering logic to support zero-shot adaptation. |
| Outcome: | The proposed framework performs better than a non-adapted parser with features and logical form candidate filtering logic. |
Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation (2021.eacl-main)
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| Challenge: | Traditional NLP has long held (supervised) syntactic parsing necessary for successful higher-level semantic language understanding (LU). |
| Approach: | They empirically examine the usefulness of supervised parsing for semantic LU in LM-pretrained transformer networks. |
| Outcome: | The proposed model is based on LM-pretrained transformer networks with a biaffine parsing head and fine-tuned for LU tasks. |