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Found 26 papers, 11 papers with code
Incorporating Zoning Information into Argument Mining from Biomedical Literature
Argumentative zoning, a specific text zoning scheme for the scientific domain, is considered as the antecedent for argument mining by many researchers.
TriG-NER: Triplet-Grid Framework for Discontinuous Named Entity Recognition
These results underscore our framework's effectiveness in capturing complex entity structures and its adaptability to various tagging schemes, setting a new benchmark for discontinuous entity extraction.
Learning to Generate and Evaluate Fact-checking Explanations with Transformers
By introducing human-centred evaluation methods and developing specialised datasets, we emphasise the need for aligning Artificial Intelligence (AI)-generated explanations with human judgements.
Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension
Performance of NLP systems is typically evaluated by collecting a large-scale dataset by means of crowd-sourcing to train a data-driven model and evaluate it on a held-out portion of the data.
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation
In our work, we introduce an argument mining dataset that captures the end-to-end process of preparing an argumentative essay for a debate, which covers the tasks of claim and evidence identification (Task 1 ED), evidence convincingness ranking (Task 2 ECR), argumentative essay summarisation and human preference ranking (Task 3 ASR) and metric learning for automated evaluation of resulting essays, based on human feedback along argument quality dimensions (Task 4 SQE).
CANTONMT: Investigating Back-Translation and Model-Switch Mechanisms for Cantonese-English Neural Machine Translation
To achieve this, a new parallel corpus has been created by combining different available corpora online with preprocessing and cleaning.
Aspect-based Sentiment Evaluation of Chess Moves (ASSESS): an NLP-based Method for Evaluating Chess Strategies from Textbooks
In this study, we examine the complicated relationships between multiple referenced moves in a chess-teaching textbook, and propose a novel method designed to encapsulate chess knowledge derived from move-action phrases.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
CantonMT: Cantonese to English NMT Platform with Fine-Tuned Models Using Synthetic Back-Translation Data
We present the models we fine-tuned using the limited amount of real data and the synthetic data we generated using back-translation including OpusMT, NLLB, and mBART.
Learning to Play Chess from Textbooks (LEAP): a Corpus for Evaluating Chess Moves based on Sentiment Analysis
This paper examines chess textbooks as a new knowledge source for enabling machines to learn how to play chess -- a resource that has not been explored previously.
TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles
Temporal relation extraction models have thus far been hindered by a number of issues in existing temporal relation-annotated news datasets, including: (1) low inter-annotator agreement due to the lack of specificity of their annotation guidelines in terms of what counts as a temporal relation; (2) the exclusion of long-distance relations within a given document (those spanning across different paragraphs); and (3) the exclusion of events that are not centred on verbs.
Towards End-User Development for IoT: A Case Study on Semantic Parsing of Cooking Recipes for Programming Kitchen Devices
Semantic parsing of user-generated instructional text, in the way of enabling end-users to program the Internet of Things (IoT), is an underexplored area.
PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models
Medical progress notes play a crucial role in documenting a patient's hospital journey, including his or her condition, treatment plan, and any updates for healthcare providers.
Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation
Furthermore, evaluating key points is crucial in ensuring that the automatically generated summaries are useful.
Natural Language Robot Programming: NLP integrated with autonomous robotic grasping
In this paper, we present a grammar-based natural language framework for robot programming, specifically for pick-and-place tasks.
Towards Human-Centred Explainability Benchmarks For Text Classification
Progress on many Natural Language Processing (NLP) tasks, such as text classification, is driven by objective, reproducible and scalable evaluation via publicly available benchmarks.
RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced Labour
Each news article was annotated for two aspects: (1) indicators of forced labour as classification labels and (2) snippets of the text that justify labelling decisions.
Multi Label Text Classification
Multi-Label Text Classification
+1
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?
We define seven MRC skills which require the understanding of different discourse relations.
Machine Reading Comprehension
Natural Language Understanding
Semantics Altering Modifications for Evaluating Comprehension in Machine Reading
Advances in NLP have yielded impressive results for the task of machine reading comprehension (MRC), with approaches having been reported to achieve performance comparable to that of humans.
Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models
Recent years have seen a growing number of publications that analyse Natural Language Inference (NLI) datasets for superficial cues, whether they undermine the complexity of the tasks underlying those datasets and how they impact those models that are optimised and evaluated on this data.
ParlVote: A Corpus for Sentiment Analysis of Political Debates
These include a linear classifier as well as a neural network trained using a transformer word embedding model (BERT), and fine-tuned on the parliamentary speeches.
A Framework for Evaluation of Machine Reading Comprehension Gold Standards
Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph of text.
Policy Preference Detection in Parliamentary Debate Motions
Debate motions (proposals) tabled in the UK Parliament contain information about the stated policy preferences of the Members of Parliament who propose them, and are key to the analysis of all subsequent speeches given in response to them.
Semantic Change in the Language of UK Parliamentary Debates
We investigate changes in the meanings of words used in the UK Parliament across two different epochs.
Sentiment and position-taking analysis of parliamentary debates: A systematic literature review
In this article we present the results of a systematic literature review of 61 studies, all of which address the automatic analysis of the sentiment and opinions expressed and positions taken by speakers in parliamentary (and other legislative) debates.
Semantic Frame Embeddings for Detecting Relations between Software Requirements
One of the more recent advances in NLP is the use of word embeddings for capturing contextual information, which can then be applied in word analogy tasks.
