# Conversational Semantic Parsing

arXiv: https://arxiv.org/abs/2009.13655 · HTML: https://mlsanity.com/paper/2009.13655/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 30 of 39)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding](https://mlsanity.com/paper/2104.02138/) (2021)
- [STOP: A dataset for Spoken Task Oriented Semantic Parsing](https://mlsanity.com/paper/2207.10643/) (2022)
- [Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric](https://mlsanity.com/paper/2110.05376/) (2021)
- [Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2104.07275/) (2021)
- [Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog](https://mlsanity.com/paper/2104.04923/) (2021)
- [RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2109.10410/) (2021)
- [Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing](https://mlsanity.com/paper/2209.14899/) (2022)
- [Explainable Abuse Detection as Intent Classification and Slot Filling](https://mlsanity.com/paper/2210.02659/) (2022)
- [Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modeling](https://mlsanity.com/paper/2104.07224/) (2021)
- [Compositional Task-Oriented Parsing as Abstractive Question Answering](https://mlsanity.com/paper/2205.02068/) (2022)
- [Dialog2API: Task-Oriented Dialogue with API Description and Example Programs](https://mlsanity.com/paper/2212.09946/) (2022)
- [Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder](https://mlsanity.com/paper/2109.04500/) (2021)
- [Deliberation Model for On-Device Spoken Language Understanding](https://mlsanity.com/paper/2204.01893/) (2022)
- [Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2202.00901/) (2022)
- [Reducing Model Jitter: Stable Re-training of Semantic Parsers in Production Environments](https://mlsanity.com/paper/2204.04735/) (2022)
- [Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing](https://mlsanity.com/paper/2305.15338/) (2023)
- [ReVision: A Dataset and Baseline VLM for Privacy-Preserving Task-Oriented Visual Instruction Rewriting](https://mlsanity.com/paper/2502.14780/) (2025)
- [Diagnosing Transformers in Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2105.13496/) (2021)
- [Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models](https://mlsanity.com/paper/2203.02652/) (2022)
- [One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets](https://mlsanity.com/paper/2106.04476/) (2021)
- [A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective](https://mlsanity.com/paper/2202.04847/) (2022)
- [Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning](https://mlsanity.com/paper/2204.06748/) (2022)
- [Dialogue Meaning Representation for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2204.10989/) (2022)
- [Cross-TOP: Zero-Shot Cross-Schema Task-Oriented Parsing](https://mlsanity.com/paper/2206.05352/) (2022)
- [Semantic-based Pre-training for Dialogue Understanding](https://mlsanity.com/paper/2209.09146/) (2022)
- [Graph-Based Decoding for Task Oriented Semantic Parsing](https://mlsanity.com/paper/2109.04587/) (2021)
- [CookDial: A dataset for task-oriented dialogs grounded in procedural documents](https://mlsanity.com/paper/2206.08723/) (2022)
- [Lexicon-injected Semantic Parsing for Task-Oriented Dialog](https://mlsanity.com/paper/2211.14508/) (2022)
- [PIZZA: A new benchmark for complex end-to-end task-oriented parsing](https://mlsanity.com/paper/2212.00265/) (2022)

## Abstract (arXiv)

The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the representation, the session-based properties such as co-reference resolution and context carryover are processed downstream in a pipelined system. In this paper, we propose a semantic representation for such task-oriented conversational systems that can represent concepts such as co-reference and context carryover, enabling comprehensive understanding of queries in a session. We release a new session-based, compositional task-oriented parsing dataset of 20k sessions consisting of 60k utterances. Unlike Dialog State Tracking Challenges, the queries in the dataset have compositional forms. We propose a new family of Seq2Seq models for the session-based parsing above, which achieve better or comparable performance to the current state-of-the-art on ATIS, SNIPS, TOP and DSTC2. Notably, we improve the best known results on DSTC2 by up to 5 points for slot-carryover.