# Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation

## TL;DR

This paper presents a hierarchical neural model with semi-supervised and adversarial training techniques to improve speech act recognition in asynchronous conversations, effectively leveraging unlabeled data and cross-domain labeled data.

## Contribution

It introduces a novel hierarchical LSTM-CRF architecture combined with semi-supervised and adversarial training methods for SAR in asynchronous conversations.

## Key findings

- Outperforms existing methods with in-domain data
- Semi-supervised learning improves model performance
- Adversarial training reduces domain shift effects

## Abstract

We address the problem of speech act recognition (SAR) in asynchronous conversations (forums, emails). Unlike synchronous conversations (e.g., meetings, phone), asynchronous domains lack large labeled datasets to train an effective SAR model. In this paper, we propose methods to effectively leverage abundant unlabeled conversational data and the available labeled data from synchronous domains. We carry out our research in three main steps. First, we introduce a neural architecture based on hierarchical LSTMs and conditional random fields (CRF) for SAR, and show that our method outperforms existing methods when trained on in-domain data only. Second, we improve our initial SAR models by semi-supervised learning in the form of pretrained word embeddings learned from a large unlabeled conversational corpus. Finally, we employ adversarial training to improve the results further by leveraging the labeled data from synchronous domains and by explicitly modeling the distributional shift in two domains.