# Zoho at SemEval-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion Mining

## TL;DR

This paper presents a semi-supervised domain adaptation method using tri-training with CNNs and contextual embeddings for suggestion mining, effectively handling domain shifts and improving cross-domain performance.

## Contribution

It introduces a tri-training approach with CNNs and contextual embeddings for semi-supervised domain adaptation in suggestion mining, addressing domain shift without labeled data.

## Key findings

- Achieved 68.07 F1-score in in-domain suggestion mining.
- Achieved 81.94 F1-score in cross-domain suggestion mining.
- Tri-training effectively improves domain adaptation performance.

## Abstract

This paper describes our submission for the SemEval-2019 Suggestion Mining task. A simple Convolutional Neural Network (CNN) classifier with contextual word representations from a pre-trained language model was used for sentence classification. The model is trained using tri-training, a semi-supervised bootstrapping mechanism for labelling unseen data. Tri-training proved to be an effective technique to accommodate domain shift for cross-domain suggestion mining (Subtask B) where there is no hand labelled training data. For in-domain evaluation (Subtask A), we use the same technique to augment the training set. Our system ranks thirteenth in Subtask A with an $F_1$-score of 68.07 and third in Subtask B with an $F_1$-score of 81.94.