---
title: "Weakly Supervised Learning of Semantic Parsers — Artzi 2013"
description: "The authors train a system to map natural-language navigation instructions to sequences of agent actions on a map."
authors: ["Yoav Artzi", "Luke Zettlemoyer"]
year: 2013
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/Q13-1005/"
doi: "10.1162/tacl_a_00209"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/artzi-2013-weakly-supervised-learning-semantic-parsers/"
---
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# Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions

**Authors:** [Yoav Artzi](https://md.research.elamur.ai/free/en/authors/yoav-artzi/), [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/) · **Year:** 2013 · **Venue:** Transactions of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/Q13-1005/) · [PDF](https://aclanthology.org/Q13-1005.pdf) · [DOI 10.1162/tacl\_a\_00209](https://doi.org/10.1162/tacl_a_00209) · [OpenAlex](https://openalex.org/W2189089430)

**Citations:** 406 citations (openalex-snapshot · 2026-10-14) · [186](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The authors train a system to map natural-language navigation instructions to sequences of agent actions on a map. No annotated logical forms are available for training. The method is built on a weighted CCG grammar with a factored lexicon. Sentence meanings are written in λ-calculus, and this notation is adapted to the spatial language of instructions: references to objects, implicit steps, and constraints such as "twice". The key idea is that the model parses a sentence and executes it in the environment at the same time. As a result, context, such as the objects visible to the agent, directly influences which interpretation is chosen. Instead of annotations, learning relies on a validation function. It checks either the entire path followed by a human or only the final position. To this end, the authors adapted a loss-sensitive Perceptron, and the lexicon is expanded via GENLEX with coarse-to-fine inference. On the SAIL corpus, the system fully executes 60% more instruction sets than the best previous approach. The work shows that a weak signal from the environment can replace costly manual semantic annotation.

## Key points
- The agent has four actions: LEFT, RIGHT, MOVE, NULL. A state is defined by grid coordinates and orientation, and the maps in the experiments contain 141 states on average.
- The initial lexicon was built manually from 12 instruction sequences, yielding 141 lexical entries.
- On SAIL (cross-validation): individual sentences are executed correctly in 65.28% of cases, and complete sequences in 31.93%. Kim & Mooney (2012) reported 57.22% and 20.17%, respectively. When only the final state is validated, the results barely drop: 64.25% and 30.9%.
- On the filtered oracle corpus (test set, trajectory validation), accuracy is 78.63% for sentences and 58.05% for sequences. Logical form (LF) accuracy is 51.05%; it was measured on 105 sentences manually annotated with logical forms.
- Without implicit actions and without joint execution during parsing (ablations), performance drops, especially on complete sequences.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [AI & machine learning](https://md.research.elamur.ai/free/en/shelves/ai/)

## Cited by

Cited by

186 papers of our library cite it (of the 140673 whose bibliographies we read); 14 of them have a page here, 172 not yet.

- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://md.research.elamur.ai/free/en/papers/chen-2019-tabfact-large-scale-dataset-table/)
- [Unified Pragmatic Models for Generating and Following Instructions](https://md.research.elamur.ai/free/en/papers/fried-2018-unified-pragmatic-models-generating-following/)
- [From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood](https://md.research.elamur.ai/free/en/papers/guu-2017-language-programs-bridging-reinforcement-learning/)
- [TaPas: Weakly Supervised Table Parsing via Pre-training](https://md.research.elamur.ai/free/en/papers/herzig-2020-tapas-weakly-supervised-table-parsing/)
- [An Analysis of Natural Language Inference Benchmarks through the Lens of Negation](https://md.research.elamur.ai/free/en/papers/hossain-2020-analysis-natural-language-inference-benchmarks/)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://md.research.elamur.ai/free/en/papers/huang-2022-language-models-zero-shot-planners/)
- [Enhancing PTB Universal Dependencies for Grammar-Based Surface Realization](https://md.research.elamur.ai/free/en/papers/king-2016-enhancing-ptb-universal-dependencies-grammar/)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://md.research.elamur.ai/free/en/papers/lee-2019-latent-retrieval-weakly-supervised-open/)
- [A Discrete Hard EM Approach for Weakly Supervised Question Answering](https://md.research.elamur.ai/free/en/papers/min-2019-discrete-hard-em-approach-weakly/)
- [LEVER: Learning to Verify Language-to-Code Generation with Execution](https://md.research.elamur.ai/free/en/papers/ni-2023-lever-learning-verify-language-code/)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://md.research.elamur.ai/free/en/papers/shridhar-2019-alfred-benchmark-interpreting-grounded-instructions/)
- [From Shakespeare to Twitter: What are Language Styles all about?](https://md.research.elamur.ai/free/en/papers/xu-2017-shakespeare-twitter-what-are-language/)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://md.research.elamur.ai/free/en/papers/yu-2018-spider-large-scale-human-labeled/)
- [SParC: Cross-Domain Semantic Parsing in Context](https://md.research.elamur.ai/free/en/papers/yu-2019-sparc-cross-domain-semantic-parsing/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{artzi2013weakly,
  title = {{Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions}},
  author = {Artzi, Yoav and Zettlemoyer, Luke},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2013},
  doi = {10.1162/tacl\_a\_00209},
  url = {https://doi.org/10.1162/tacl_a_00209}
}
```

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