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Found 22 papers, 7 papers with code
Demonstrating the Reliability of Self-Annotated Emotion Data
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.
Investigating Metric Diversity for Evaluating Long Document Summarisation
Long document summarisation, a challenging summarisation scenario, is the focus of the recently proposed LongSumm shared task.
Measuring Similarity of Opinion-bearing Sentences
For many NLP applications of online reviews, comparison of two opinion-bearing sentences is key.
What Causes the Failure of Explicit to Implicit Discourse Relation Recognition?
We consider an unanswered question in the discourse processing community: why do relation classifiers trained on explicit examples (with connectives removed) perform poorly in real implicit scenarios?
Mention Flags (MF): Constraining Transformer-based Text Generators
In this paper, we propose Mention Flags (MF), which traces whether lexical constraints are satisfied in the generated outputs in an S2S decoder.
Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation
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.
Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction
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.
ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning
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.
Towards Generating Stylized Image Captions via Adversarial Training
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).
Image Captioning using Facial Expression and Attention
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.
How to best use Syntax in Semantic Role Labelling
There are many different ways in which external information might be used in an NLP task.
Red-faced ROUGE: Examining the Suitability of ROUGE for Opinion Summary Evaluation
Opinion summarisation requires to correctly pair two types of semantic information: (1) aspect or opinion target; and (2) polarity of candidate and reference summaries.
Senti-Attend: Image Captioning using Sentiment and Attention
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.
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
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)
Demographic Inference on Twitter using Recursive Neural Networks
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.
CSIRO Data61 at the WNUT Geo Shared Task
In this paper, we describe CSIRO Data61{'}s participation in the Geolocation shared task at the Workshop for Noisy User-generated Text.
