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Found 78 papers, 35 papers with code
Automatic Detection and Language Identification of Multilingual Documents
Language identification is the task of automatically detecting the language(s) present in a document based on the content of the document.
An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation
Recently, Le and Mikolov (2014) proposed doc2vec as an extension to word2vec (Mikolov et al., 2013a) to learn document-level embeddings.
Automatic Labelling of Topics with Neural Embeddings
Topics generated by topic models are typically represented as list of terms.
Multimodal Topic Labelling
Automatic topic labelling is the task of generating a succinct label that summarises the theme or subject of a topic, with the intention of reducing the cognitive load of end-users when interpreting these topics.
Decoupling Encoder and Decoder Networks for Abstractive Document Summarization
With this decoupled architecture, we decrease the number of parameters in the decoder substantially, and shorten its training time.
Topically Driven Neural Language Model
Language models are typically applied at the sentence level, without access to the broader document context.
An Automatic Approach for Document-level Topic Model Evaluation
Topic models jointly learn topics and document-level topic distribution.
End-to-end Network for Twitter Geolocation Prediction and Hashing
We propose an end-to-end neural network to predict the geolocation of a tweet.
Evaluating Word Embedding Hyper-Parameters for Similarity and Analogy Tasks
The versatility of word embeddings for various applications is attracting researchers from various fields.
Document Chunking and Learning Objective Generation for Instruction Design
Instructional Systems Design is the practice of creating of instructional experiences that make the acquisition of knowledge and skill more efficient, effective, and appealing.
The Influence of Context on Sentence Acceptability Judgements
We investigate the influence that document context exerts on human acceptability judgements for English sentences, via two sets of experiments.
Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme
In this paper, we propose a joint architecture that captures language, rhyme and meter for sonnet modelling.
Topic Intrusion for Automatic Topic Model Evaluation
Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications.
Preferred Answer Selection in Stack Overflow: Better Text Representations ... and Metadata, Metadata, Metadata
Community question answering (cQA) forums provide a rich source of data for facilitating non-factoid question answering over many technical domains.
From Shakespeare to Li-Bai: Adapting a Sonnet Model to Chinese Poetry
In this paper, we adapt Deep-speare, a joint neural network model for English sonnets, to Chinese poetry.
Early Rumour Detection
Motivated by this, our paper focuses on the task of rumour detection; particularly, we are interested in understanding how early we can detect them.
Improved Document Modelling with a Neural Discourse Parser
We empirically investigate the benefit of the proposed approach on two different tasks: abstractive summarization and popularity prediction of online petitions.
How Furiously Can Colorless Green Ideas Sleep? Sentence Acceptability in Context
We study the influence of context on sentence acceptability.
Elephant in the Room: An Evaluation Framework for Assessing Adversarial Examples in NLP
An adversarial example is an input transformed by small perturbations that machine learning models consistently misclassify.
How Furiously Can Colourless Green Ideas Sleep? Sentence Acceptability in Context
We study the influence of context on sentence acceptability.
You are right. I am ALARMED -- But by Climate Change Counter Movement
The world is facing the challenge of climate crisis.
Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis?
We focus in particular on the role of data statements in ethically assessing research, but also discuss the topic of dual use, and examine the outcomes of similar debates in other scientific disciplines.
Less is More: Rejecting Unreliable Reviews for Product Question Answering
In the literature, PQA is formulated as a retrieval problem with the goal to search for the most relevant reviews to answer a given product question.
COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research
We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on several distinct text analysis and natural language processing methods to structure and organise information in publications, and augments search by providing a visual overview supporting exploration of a collection to identify key articles of interest.
Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora
We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e. g. a few hundred sentence pairs).
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+4
IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP
Although the Indonesian language is spoken by almost 200 million people and the 10th most spoken language in the world, it is under-represented in NLP research.
Liputan6: A Large-scale Indonesian Dataset for Text Summarization
In this paper, we introduce a large-scale Indonesian summarization dataset.
FFCI: A Framework for Interpretable Automatic Evaluation of Summarization
In this paper, we propose FFCI, a framework for fine-grained summarization evaluation that comprises four elements: faithfulness (degree of factual consistency with the source), focus (precision of summary content relative to the reference), coverage (recall of summary content relative to the reference), and inter-sentential coherence (document fluency between adjacent sentences).
Top-down Discourse Parsing via Sequence Labelling
We introduce a top-down approach to discourse parsing that is conceptually simpler than its predecessors (Kobayashi et al., 2020; Zhang et al., 2020).
Ranked #7 on
Discourse Parsing
on RST-DT
(Standard Parseval (Span) metric)
Grey-box Adversarial Attack And Defence For Sentiment Classification
We introduce a grey-box adversarial attack and defence framework for sentiment classification.
Discourse Probing of Pretrained Language Models
Existing work on probing of pretrained language models (LMs) has predominantly focused on sentence-level syntactic tasks.
Impact of detecting clinical trial elements in exploration of COVID-19 literature
The COVID-19 pandemic has driven ever-greater demand for tools which enable efficient exploration of biomedical literature.
Automatic Classification of Neutralization Techniques in the Narrative of Climate Change Scepticism
Neutralisation techniques, e. g. denial of responsibility and denial of victim, are used in the narrative of climate change scepticism to justify lack of action or to promote an alternative view.
Evaluating the Efficacy of Summarization Evaluation across Languages
We take a summarization corpus for eight different languages, and manually annotate generated summaries for focus (precision) and coverage (recall).
Automatic Claim Review for Climate Science via Explanation Generation
There is unison is the scientific community about human induced climate change.
IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization
We present IndoBERTweet, the first large-scale pretrained model for Indonesian Twitter that is trained by extending a monolingually-trained Indonesian BERT model with additive domain-specific vocabulary.
Rumour Detection via Zero-shot Cross-lingual Transfer Learning
Most rumour detection models for social media are designed for one specific language (mostly English).
Exploring Story Generation with Multi-task Objectives in Variational Autoencoders
GPT-2 has been frequently adapted in story generation models as it provides powerful generative capability.
Findings on Conversation Disentanglement
Conversation disentanglement, the task to identify separate threads in conversations, is an important pre-processing step in multi-party conversational NLP applications such as conversational question answering and conversation summarization.
Conversational Question Answering
Conversation Disentanglement
+2
ITTC @ TREC 2021 Clinical Trials Track
This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the TREC 2021 Clinical Trials Track.
PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization
We present PeerSum, a new MDS dataset using peer reviews of scientific publications.
An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation
To obtain a transparent reasoning process, we introduce neuro-symbolic to perform explicit reasoning that justifies model decisions by reasoning chains.
One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia
NLP research is impeded by a lack of resources and awareness of the challenges presented by underrepresented languages and dialects.
Robust Task-Oriented Dialogue Generation with Contrastive Pre-training and Adversarial Filtering
Data artifacts incentivize machine learning models to learn non-transferable generalizations by taking advantage of shortcuts in the data, and there is growing evidence that data artifacts play a role for the strong results that deep learning models achieve in recent natural language processing benchmarks.
NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages
In this work, we focus on developing resources for languages in Indonesia.
Unsupervised Lexical Substitution with Decontextualised Embeddings
We propose a new unsupervised method for lexical substitution using pre-trained language models.
LED down the rabbit hole: exploring the potential of global attention for biomedical multi-document summarisation
In this paper we report on our submission to the Multidocument Summarisation for Literature Review (MSLR) shared task.
Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation
Negation is poorly captured by current language models, although the extent of this problem is not widely understood.
The Next Chapter: A Study of Large Language Models in Storytelling
To enhance the quality of generated stories, recent story generation models have been investigating the utilization of higher-level attributes like plots or commonsense knowledge.
Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization
We propose HGSUM, an MDS model that extends an encoder-decoder architecture, to incorporate a heterogeneous graph to represent different semantic units (e. g., words and sentences) of the documents.
MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters
State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale.
DeltaScore: Fine-Grained Story Evaluation with Perturbations
Numerous evaluation metrics have been developed for natural language generation tasks, but their effectiveness in evaluating stories is limited as they are not specifically tailored to assess intricate aspects of storytelling, such as fluency and interestingness.
Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation
We present PeerSum, a novel dataset for generating meta-reviews of scientific papers.
Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization
A series of datasets and models have been proposed for summaries generated for well-formatted documents such as news articles.
Unsupervised Paraphrasing of Multiword Expressions
We propose an unsupervised approach to paraphrasing multiword expressions (MWEs) in context.
Unsupervised Lexical Simplification with Context Augmentation
We propose a new unsupervised lexical simplification method that uses only monolingual data and pre-trained language models.
CMA-R:Causal Mediation Analysis for Explaining Rumour Detection
We apply causal mediation analysis to explain the decision-making process of neural models for rumour detection on Twitter.
Exploring Multi-Document Information Consolidation for Scientific Sentiment Summarization
Modern natural language generation systems with LLMs exhibit the capability to generate a plausible summary of multiple documents; however, it is uncertain if models truly possess the ability of information consolidation to generate summaries, especially on those source documents with opinionated information.
LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization
Summaries, keyphrases, and titles are different ways of concisely capturing the content of a document.
Cross-linguistic Comparison of Linguistic Feature Encoding in BERT Models for Typologically Different Languages
Though recently there have been an increased interest in how pre-trained language models encode different linguistic features, there is still a lack of systematic comparison between languages with different morphology and syntax.
DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks
Social media rumours, a form of misinformation, can mislead the public and cause significant economic and social disruption.
Easy-First Bottom-Up Discourse Parsing via Sequence Labelling
We propose a novel unconstrained bottom-up approach for rhetorical discourse parsing based on sequence labelling of adjacent pairs of discourse units (DUs), based on the framework of Koto et al. (2021).
Cloze Evaluation for Deeper Understanding of Commonsense Stories in Indonesian
Story comprehension that involves complex causal and temporal relations is a critical task in NLP, but previous studies have focused predominantly on English, leaving open the question of how the findings generalize to other languages, such as Indonesian.
The patient is more dead than alive: exploring the current state of the multi-document summarisation of the biomedical literature
Although multi-document summarisation (MDS) of the biomedical literature is a highly valuable task that has recently attracted substantial interest, evaluation of the quality of biomedical summaries lacks consistency and transparency.
Can Pretrained Language Models Generate Persuasive, Faithful, and Informative Ad Text for Product Descriptions?
For any e-commerce service, persuasive, faithful, and informative product descriptions can attract shoppers and improve sales.
Semi-automatic Triage of Requests for Free Legal Assistance
Free legal assistance is critically under-resourced, and many of those who seek legal help have their needs unmet.
