# Convex Polytope Modelling for Unsupervised Derivation of Semantic Structure for Data-efficient Natural Language Understanding

arXiv: https://arxiv.org/abs/2201.10588 · HTML: https://mlsanity.com/paper/2201.10588/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience](https://mlsanity.com/paper/1801.05032/) (2018)
- [Intent Mining from past conversations for conversational agent](https://mlsanity.com/paper/2005.11014/) (2020)
- [Dialog Intent Induction with Deep Multi-View Clustering](https://mlsanity.com/paper/1908.11487/) (2019)
- [Bootstrapping Conversational Agents With Weak Supervision](https://mlsanity.com/paper/1812.06176/) (2018)
- [Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks](https://mlsanity.com/paper/1609.03286/) (2016)
- [Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks](https://mlsanity.com/paper/1807.08207/) (2018)

## Built on by (most cited first, 1 of 1)

- [Injecting linguistic knowledge into BERT for Dialogue State Tracking](https://mlsanity.com/paper/2311.15623/) (2023)

## Abstract (arXiv)

Popular approaches for Natural Language Understanding (NLU) usually rely on a huge amount of annotated data or handcrafted rules, which is laborious and not adaptive to domain extension. We recently proposed a Convex-Polytopic-Model-based framework that shows great potential in automatically extracting semantic patterns by exploiting the raw dialog corpus. The extracted semantic patterns can be used to generate semantic frames, which is essential in assisting NLU tasks. This paper further studies the CPM model in depth and visualizes its high interpretability and transparency at various levels. We show that this framework can exploit semantic-frame-related features in the corpus, reveal the underlying semantic structure of the utterances, and boost the performance of the state-of-the-art NLU model with minimal supervision. We conduct our experiments on the ATIS (Air Travel Information System) corpus.