1 code implementation • EMNLP (newsum) 2021 • Wenyi Tay, Xiuzhen Zhang, Stephen Wan, Sarvnaz Karimi
For many NLP applications of online reviews, comparison of two opinion-bearing sentences is key.
no code implementations • NAACL (CLPsych) 2021 • Anton Malko, Cecile Paris, Andreas Duenser, Maria Kangas, Diego Molla, Ross Sparks, Stephen Wan
Vent is a specialised iOS/Android social media platform with the stated goal to encourage people to post about their feelings and explicitly label them.
1 code implementation • sdp (COLING) 2022 • Cai Yang, Stephen Wan
Long document summarisation, a challenging summarisation scenario, is the focus of the recently proposed LongSumm shared task.
1 code implementation • ACL 2021 • YuFei Wang, Ian Wood, Stephen Wan, Mark Dras, Mark Johnson
In this paper, we propose Mention Flags (MF), which traces whether lexical constraints are satisfied in the generated outputs in an S2S decoder.
no code implementations • NeurIPS 2021 • YuFei Wang, Can Xu, Huang Hu, Chongyang Tao, Stephen Wan, Mark Dras, Mark Johnson, Daxin Jiang
Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e. g., BART and T5), have exhibited compelling performance on various natural language generation tasks.
1 code implementation • NAACL 2021 • Ian Wood, Mark Johnson, Stephen Wan
OpenKi[1] addresses this task through extraction of named entities and predicates via OpenIE tools then learning relation embeddings from the resulting entity-relation graph for relation prediction, outperforming previous approaches.
no code implementations • EACL 2021 • YuFei Wang, Ian D. Wood, Stephen Wan, Mark Johnson
In this paper, we focus on this challenge and propose the ECOL-R model (Encouraging Copying of Object Labels with Reinforced Learning), a copy-augmented transformer model that is encouraged to accurately describe the novel object labels.
no code implementations • 8 Aug 2019 • Omid Mohamad Nezami, Mark Dras, Stephen Wan, Cecile Paris, Len Hamey
While most image captioning aims to generate objective descriptions of images, the last few years have seen work on generating visually grounded image captions which have a specific style (e. g., incorporating positive or negative sentiment).
no code implementations • 8 Aug 2019 • Omid Mohamad Nezami, Mark Dras, Stephen Wan, Cecile Paris
An analysis of the generated captions finds that, perhaps unexpectedly, the improvement in caption quality appears to come not from the addition of adjectives linked to emotional aspects of the images, but from more variety in the actions described in the captions.
1 code implementation • ACL 2019 • Yufei Wang, Mark Johnson, Stephen Wan, Yifang Sun, Wei Wang
There are many different ways in which external information might be used in an NLP task.
no code implementations • ALTA 2019 • Wenyi Tay, Aditya Joshi, Xiuzhen Zhang, Sarvnaz Karimi, Stephen Wan
Opinion summarisation requires to correctly pair two types of semantic information: (1) aspect or opinion target; and (2) polarity of candidate and reference summaries.
no code implementations • 24 Nov 2018 • Omid Mohamad Nezami, Mark Dras, Stephen Wan, Cecile Paris
However, such models typically have difficulty in balancing the semantic aspects of the image and the non-factual dimensions of the caption; in addition, it can be observed that humans may focus on different aspects of an image depending on the chosen sentiment or style of the caption.
3 code implementations • 7 Aug 2018 • Omid Mohamad Nezami, Mark Dras, Len Hamey, Deborah Richards, Stephen Wan, Cecile Paris
This paper presents a deep learning model to improve engagement recognition from images that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data.
Facial Expression Recognition
Facial Expression Recognition (FER)
no code implementations • ACL 2017 • Sunghwan Mac Kim, Qiongkai Xu, Lizhen Qu, Stephen Wan, C{\'e}cile Paris
In social media, demographic inference is a critical task in order to gain a better understanding of a cohort and to facilitate interacting with one{'}s audience.
no code implementations • WS 2016 • Gaya Jayasinghe, Brian Jin, James Mchugh, Bella Robinson, Stephen Wan
In this paper, we describe CSIRO Data61{'}s participation in the Geolocation shared task at the Workshop for Noisy User-generated Text.