Joanne Boisson


TweetNLP: Cutting-Edge Natural Language Processing for Social Media
Jose Camacho-collados | Kiamehr Rezaee | Talayeh Riahi | Asahi Ushio | Daniel Loureiro | Dimosthenis Antypas | Joanne Boisson | Luis Espinosa Anke | Fangyu Liu | Eugenio Martínez Cámara
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. Task-specific systems are powered by reasonably-sized Transformer-based language models specialized on social media text (in particular, Twitter) which can be run without the need for dedicated hardware or cloud services. The main contributions of TweetNLP are: (1) an integrated Python library for a modern toolkit supporting social media analysis using our various task-specific models adapted to the social domain; (2) an interactive online demo for codeless experimentation using our models; and (3) a tutorial covering a wide variety of typical social media applications.

CardiffNLP-Metaphor at SemEval-2022 Task 2: Targeted Fine-tuning of Transformer-based Language Models for Idiomaticity Detection
Joanne Boisson | Jose Camacho-Collados | Luis Espinosa-Anke
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper describes the experiments ran for SemEval-2022 Task 2, subtask A, zero-shot and one-shot settings for idiomaticity detection. Our main approach is based on fine-tuning transformer-based language models as a baseline to perform binary classification. Our system, CardiffNLP-Metaphor, ranked 8th and 7th (respectively on zero- and one-shot settings on this task. Our main contribution lies in the extensive evaluation of transformer-based language models and various configurations, showing, among others, the potential of large multilingual models over base monolingual models. Moreover, we analyse the impact of various input parameters, which offer interesting insights on how language models work in practice.


Learning Sentential Patterns of Various Rhetoric Moves for Assisted Academic Writing
Jim Chang | Hsiang-Ling Hsu | Joanne Boisson | Hao-Chun Peng | Yu-Hsuan Wu | Jason S. Chang
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters

WriteAhead: Mining Grammar Patterns in Corpora for Assisted Writing
Tzu-Hsi Yen | Jian-Cheng Wu | Jim Chang | Joanne Boisson | Jason Chang
Proceedings of ACL-IJCNLP 2015 System Demonstrations


CoNLL-2013 Shared Task: Grammatical Error Correction NTHU System Description
Ting-Hui Kao | Yu-Wei Chang | Hsun-Wen Chiu | Tzu-Hsi Yen | Joanne Boisson | Jian-Cheng Wu | Jason S. Chang
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task

Linggle: a Web-scale Linguistic Search Engine for Words in Context
Joanne Boisson | Ting-Hui Kao | Jian-Cheng Wu | Tzu-Hsi Yen | Jason S. Chang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations