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Found 135 papers, 75 papers with code
Development of a Benchmark Corpus to Support Entity Recognition in Job Descriptions
We present the development of a benchmark suite consisting of an annotation schema, training corpus and baseline model for Entity Recognition (ER) in job descriptions, published under a Creative Commons license.
CM-Gen: A Neural Framework for Chinese Metaphor Generation with Explicit Context Modelling
The metaphor identification module is able to perform a self-training procedure, which discovers novel metaphors from a large-scale unlabeled corpus for NM generation.
DocMMIR: A Framework for Document Multi-modal Information Retrieval
The rapid advancement of unsupervised representation learning and large-scale pre-trained vision-language models has significantly improved cross-modal retrieval tasks.
ContrastScore: Towards Higher Quality, Less Biased, More Efficient Evaluation Metrics with Contrastive Evaluation
In this work, we introduce ContrastScore, a contrastive evaluation metric designed to enable higher-quality, less biased, and more efficient assessment of generated text.
Natural Language Generation
The term Natural Language Generation (NLG), in its broadest definition, refers to the study of systems that verbalize some form of information through natural language.
YuE: Scaling Open Foundation Models for Long-Form Music Generation
We tackle the task of long-form music generation--particularly the challenging \textbf{lyrics-to-song} problem--by introducing YuE, a family of open foundation models based on the LLaMA2 architecture.
Does Acceleration Cause Hidden Instability in Vision Language Models? Uncovering Instance-Level Divergence Through a Large-Scale Empirical Study
Our findings are stark: despite minimal aggregate performance drops, accelerated models changed original answers up to 20% of the time.
Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation
With the advent of large multimodal language models, science is now at a threshold of an AI-based technological transformation.
LVPruning: An Effective yet Simple Language-Guided Vision Token Pruning Approach for Multi-modal Large Language Models
Multi-modal Large Language Models (MLLMs) have achieved remarkable success by integrating visual and textual modalities.
Does Table Source Matter? Benchmarking and Improving Multimodal Scientific Table Understanding and Reasoning
While multimodal large language models (MLLMs) enable direct visual processing, they face limitations in handling scientific tables due to fixed input image resolutions and insufficient numerical reasoning capabilities.
Leveraging Large Language Models for Zero-shot Lay Summarisation in Biomedicine and Beyond
In this work, we explore the application of Large Language Models to zero-shot Lay Summarisation.
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks
Recent advancements in natural language processing have highlighted the vulnerability of deep learning models to adversarial attacks.
Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment
Aligning Large Language Models (LLMs) with general human preferences has been proved crucial in improving the interaction quality between LLMs and human.
Observing Micromotives and Macrobehavior of Large Language Models
Thomas C. Schelling, awarded the 2005 Nobel Memorial Prize in Economic Sciences, pointed out that ``individuals decisions (micromotives), while often personal and localized, can lead to societal outcomes (macrobehavior) that are far more complex and different from what the individuals intended.''
Lightweight Contenders: Navigating Semi-Supervised Text Mining through Peer Collaboration and Self Transcendence
The semi-supervised learning (SSL) strategy in lightweight models requires reducing annotated samples and facilitating cost-effective inference.
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling
Experiments on news benchmark datasets and one Twitter dataset demonstrate the method's superiority in generating coherent, diverse topics, and handling noisy data, outperforming strong baselines.
Can MLLMs Understand the Deep Implication Behind Chinese Images?
To fill the gap, we introduce the **C**hinese **I**mage **I**mplication understanding **Bench**mark, **CII-Bench**, which aims to assess the higher-order perception and understanding capabilities of MLLMs for Chinese images.
A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
In our work, to investigate the reasoning patterns of o1, we compare o1 with existing Test-time Compute methods (BoN, Step-wise BoN, Agent Workflow, and Self-Refine) by using OpenAI's GPT-4o as a backbone on general reasoning benchmarks in three domains (i. e., math, coding, commonsense reasoning).
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference
This research contributes: (1) A methodology for increasing question difficulty using PPO and synthetic data; (2) Empirical evidence of the method's effectiveness, including human evaluation; (3) An in-depth error analysis and study of emergent phenomena; and (4) An open-source codebase and set of three llama-2-chat adapters for reproducibility and adaptation.
On the Rigour of Scientific Writing: Criteria, Analysis, and Insights
Rigour is crucial for scientific research as it ensures the reproducibility and validity of results and findings.
From Facts to Insights: A Study on the Generation and Evaluation of Analytical Reports for Deciphering Earnings Calls
This paper explores the use of Large Language Models (LLMs) in the generation and evaluation of analytical reports derived from Earnings Calls (ECs).
Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking
As a promising alternative to human intuition and brute-force fine-tuning, Transferability Estimation (TE) has emerged as an effective approach to model selection.
With Ears to See and Eyes to Hear: Sound Symbolism Experiments with Multimodal Large Language Models
To investigate this, we analyse the ability of VLMs and LLMs to demonstrate sound symbolism (i. e., to recognise a non-arbitrary link between sounds and concepts) as well as their ability to "hear" via the interplay of the language and vision modules of open and closed-source multimodal models.
OmniBench: Towards The Future of Universal Omni-Language Models
Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored.
LIME: Less Is More for MLLM Evaluation
However, many of these benchmarks include overly simple or uninformative samples, complicating the effective distinction of different MLLMs' performance.
Foundation Models for Music: A Survey
In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music.
Overview of the BioLaySumm 2024 Shared Task on the Lay Summarization of Biomedical Research Articles
This paper presents the setup and results of the second edition of the BioLaySumm shared task on the Lay Summarisation of Biomedical Research Articles, hosted at the BioNLP Workshop at ACL 2024.
I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm
Large Language Models (LLMs) have achieved significant advancements, however, the common learning paradigm treats LLMs as passive information repositories, neglecting their potential for active learning and alignment.
Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation
The goal of this shared task is to generate Chinese metaphors using machine learning techniques and effectively identifying basic components of metaphorical sentences.
MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models
Moreover, we explored the ability of LVLMs to perceive image sequences within the context of our multi-image association task.
PFME: A Modular Approach for Fine-grained Hallucination Detection and Editing of Large Language Models
In editing tasks, PFME further enhances the FActScore of FActScore-Alpaca13B and FActScore-ChatGPT datasets, increasing by 16. 2pp and 4. 6pp, respectively.
BioMNER: A Dataset for Biomedical Method Entity Recognition
Named entity recognition (NER) stands as a fundamental and pivotal task within the realm of Natural Language Processing.
X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation
We show that training on the layman's terms dataset encourages models to focus on the semantics of the reports, as opposed to overfitting to learning the report templates.
Crafting Customisable Characters with LLMs: Introducing SimsChat, a Persona-Driven Role-Playing Agent Framework
Large Language Models (LLMs) demonstrate remarkable ability to comprehend instructions and generate human-like text, enabling sophisticated agent simulation beyond basic behavior replication.
MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language
Machine Translation (MT) has developed rapidly since the release of Large Language Models and current MT evaluation is performed through comparison with reference human translations or by predicting quality scores from human-labeled data.
ATLAS: Improving Lay Summarisation with Attribute-based Control
Lay summarisation aims to produce summaries of scientific articles that are comprehensible to non-expert audiences.
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
To improve the transparency of LLMs, the research community has formed to open-source truly open LLMs (e. g., Pythia, Amber, OLMo), where more details (e. g., pre-training corpus and training code) are being provided.
SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation
Our approach introduces several techniques: (1) Contrastive learning to differentiate between robust and non-robust response embeddings; (2) A novel metric for semantic sensitivity that combines embedding cosine distances with similarity learned through neural networks, and (3) a strategy for incorporating the evaluation results from both the SLM and LLMs.
ComposerX: Multi-Agent Symbolic Music Composition with LLMs
Music composition represents the creative side of humanity, and itself is a complex task that requires abilities to understand and generate information with long dependency and harmony constraints.
ReproHum #0087-01: Human Evaluation Reproduction Report for Generating Fact Checking Explanations
This paper presents a partial reproduction of Generating Fact Checking Explanations by Anatanasova et al (2020) as part of the ReproHum element of the ReproNLP shared task to reproduce the findings of NLP research regarding human evaluation.
MuPT: A Generative Symbolic Music Pretrained Transformer
In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music.
Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation
Trainable evaluation metrics, typically trained with true positive and randomly selected negative responses, tend to assign higher scores to responses that share greater content similarity with a given context.
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
Remarkable progress on English instruction tuning has facilitated the efficacy and reliability of large language models (LLMs).
Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases
Previous work in phonologically and phonetically grounded language generation has mainly focused on domains such as puns and poetry.
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning
Our framework employs a dual 3B model approach, with each model assigned a distinct role: one focuses on task definition extraction, while the other handles learning from demonstrations.
ChatMusician: Understanding and Generating Music Intrinsically with LLM
It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language.
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-following.
CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation
These models are able to generate creative and fluent metaphor sentences more frequently induced by selected samples from our dataset, demonstrating the value of our corpus for Chinese metaphor research.
Pixel Sentence Representation Learning
To our knowledge, this is the first representation learning method devoid of traditional language models for understanding sentence and document semantics, marking a stride closer to human-like textual comprehension.
Evaluating Large Language Models for Generalization and Robustness via Data Compression
We measure: 1) the compression performance on the testing period as a measure of generalization on unseen data; and 2) the performance gap between the training and testing period as a measure of robustness.
Finding Challenging Metaphors that Confuse Pretrained Language Models
In this paper, we test various NLP models on the VUA metaphor dataset and quantify to what extent metaphors affect models' performance on various downstream tasks.
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval
We further annotate the image-text pairs with two-level subset-subcategory hierarchy annotations to facilitate a more comprehensive evaluation of the baselines.
CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark
We introduce CMMMU, a new Chinese Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate LMMs on tasks demanding college-level subject knowledge and deliberate reasoning in a Chinese context.
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation
In this paper, we introduce Kun, a novel approach for creating high-quality instruction-tuning datasets for large language models (LLMs) without relying on manual annotations.
Language Model as an Annotator: Unsupervised Context-aware Quality Phrase Generation
Finally, we merge the quality phrases from both the Annotator and Generator as the final predictions, considering their complementary nature and distinct characteristics.
LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test Construction
LatestEval avoids data contamination by only using texts published within a recent time window, ensuring no overlap with the training corpora of pre-trained language models.
How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey
However, there is a lack of a comprehensive comparison between these estimation methods yet.
A Cross-Attention Augmented Model for Event-Triggered Context-Aware Story Generation
These results underscore the effectiveness of our model in leveraging context and event features to improve the quality of generated narratives.
LLMs as Narcissistic Evaluators: When Ego Inflates Evaluation Scores
Automatic evaluation of generated textual content presents an ongoing challenge within the field of NLP.
The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation
Human evaluation is often considered to be the gold standard method of evaluating a Natural Language Generation system.
ACL Anthology Helper: A Tool to Retrieve and Manage Literature from ACL Anthology
This paper presents a tool called ``ACL Anthology Helper''.
An Open Source Data Contamination Report for Large Language Models
We also introduce an open-source pipeline that enables the community to perform contamination analysis on customised data and models.
Enhancing Biomedical Lay Summarisation with External Knowledge Graphs
Previous approaches for automatic lay summarisation are exclusively reliant on the source article that, given it is written for a technical audience (e. g., researchers), is unlikely to explicitly define all technical concepts or state all of the background information that is relevant for a lay audience.
Length is a Curse and a Blessing for Document-level Semantics
In recent years, contrastive learning (CL) has been extensively utilized to recover sentence and document-level encoding capability from pre-trained language models.
Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers
Abstracts derived from biomedical literature possess distinct domain-specific characteristics, including specialised writing styles and biomedical terminologies, which necessitate a deep understanding of the related literature.
Compressing Context to Enhance Inference Efficiency of Large Language Models
This paper proposes a method called Selective Context that enhances the inference efficiency of LLMs by identifying and pruning redundancy in the input context to make the input more compact.
Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles
This paper presents the results of the shared task on Lay Summarisation of Biomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL 2023.
Effective Distillation of Table-based Reasoning Ability from LLMs
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks.
Audio Contrastive based Fine-tuning
Audio classification plays a crucial role in speech and sound processing tasks with a wide range of applications.
Improving Medical Dialogue Generation with Abstract Meaning Representations
In this paper, We propose a novel framework that models dialogues between patients and healthcare professionals using AMR graphs, where the neural networks incorporate textual and graphical knowledge with a dual attention mechanism.
On the Effectiveness of Speech Self-supervised Learning for Music
Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech.
LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT
We introduce LyricWhiz, a robust, multilingual, and zero-shot automatic lyrics transcription method achieving state-of-the-art performance on various lyrics transcription datasets, even in challenging genres such as rock and metal.
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation
Further analysis also shows that our representation learning framework can fill the semantic gap by coagulating representations of both text and graph knowledge.
MARBLE: Music Audio Representation Benchmark for Universal Evaluation
This is evident in the limited work on deep music representations, the scarcity of large-scale datasets, and the absence of a universal and community-driven benchmark.
TwistList: Resources and Baselines for Tongue Twister Generation
Previous work in phonetically-grounded language generation has mainly focused on domains such as lyrics and poetry.
MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training
Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored.
Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information
The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i. e., there may be multiple suitable responses which differ in semantics for a given conversational context.
Metaphor Detection via Explicit Basic Meanings Modelling
One noticeable trend in metaphor detection is the embrace of linguistic theories such as the metaphor identification procedure (MIP) for model architecture design.
Interactive Natural Language Processing
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with the ultimate goals of artificial intelligence.
DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining
Many text mining models are constructed by fine-tuning a large deep pre-trained language model (PLM) in downstream tasks.
CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation
Commonsense knowledge is crucial to many natural language processing tasks.
Chinese Open Instruction Generalist: A Preliminary Release
Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat. openai. com/}}.
Requirement Formalisation using Natural Language Processing and Machine Learning: A Systematic Review
We found that heuristic NLP approaches are the most common NLP techniques used for automatic RF, primary operating on structured and semi-structured data.
Metaphor Detection with Effective Context Denoising
We propose a novel RoBERTa-based model, RoPPT, which introduces a target-oriented parse tree structure in metaphor detection.
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning
In this paper, we propose FrameBERT, a RoBERTa-based model that can explicitly learn and incorporate FrameNet Embeddings for concept-level metaphor detection.
The Secret of Metaphor on Expressing Stronger Emotion
Metaphors are proven to have stronger emotional impact than literal expressions.
CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation
As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese.
Routine Outcome Monitoring in Psychotherapy Treatment using Sentiment-Topic Modelling Approach
While outcome monitoring tends to improve the therapy outcomes, however, there are many challenges in the current method, e. g. time and financial burden for administering questionnaires, scoring and analysing the results.
MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning
The deep learning community has witnessed an exponentially growing interest in self-supervised learning (SSL).
HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models
In addition to verifying the existence of regional bias in LMs, we find that the biases on regional groups can be strongly influenced by the geographical clustering of the groups.
Improving Variational Autoencoders with Density Gap-based Regularization
There are effective ways proposed to prevent posterior collapse in VAEs, but we observe that they in essence make trade-offs between posterior collapse and hole problem, i. e., mismatch between the aggregated posterior distribution and the prior distribution.
Terminology-aware Medical Dialogue Generation
In this paper, we propose a novel framework to improve medical dialogue generation by considering features centered on domain-specific terminology.
EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention
One of the key challenges of automatic story generation is how to generate a long narrative that can maintain fluency, relevance, and coherence.
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics
In spite of the success of prior works with the application of pre-trained models, current neural models for Chinese stories still struggle to generate high-quality long text narratives.
NGEP: A Graph-based Event Planning Framework for Story Generation
To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i. e. storylines) to guide story generation.
Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
Lay summarisation aims to jointly summarise and simplify a given text, thus making its content more comprehensible to non-experts.
Ranked #1 on
Lay Summarization
on PLOS
PUF-Phenotype: A Robust and Noise-Resilient Approach to Aid Intra-Group-based Authentication with DRAM-PUFs Using Machine Learning
As the demand for highly secure and dependable lightweight systems increases in the modern world, Physically Unclonable Functions (PUFs) continue to promise a lightweight alternative to high-cost encryption techniques and secure key storage.
Nominal Metaphor Generation with Multitask Learning
Metaphor generation is a challenging task which can impact many downstream tasks such as improving user satisfaction with dialogue systems and story generation.
TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction
However, such a method strictly restricts entities on the hyper-ellipsoid surfaces which limits the optimization of entity distribution, leading to suboptimal performance of knowledge graph completion.
Recent Advances in Neural Text Generation: A Task-Agnostic Survey
In recent years, considerable research has been dedicated to the application of neural models in the field of natural language generation (NLG).
Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation
In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query.
On the Latent Holes of VAEs for Text Generation
In this paper, we provide the first focused study on the discontinuities (aka.
On the Latent Holes 🧀 of VAEs for Text Generation
In this paper, we provide the first focused study on the discontinuities (aka.
Extractive and Abstractive Sentence Labelling of Sentiment-bearing Topics
Our experimental results on three real-world datasets show that both the extractive and abstractive approaches outperform four strong baselines in terms of facilitating topic understanding and interpretation.
Affective Decoding for Empathetic Response Generation
Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems.
Cross-Lingual Word Embedding Refinement by $\ell_1$ Norm Optimisation
It is therefore recommended that this strategy be adopted as a standard for CLWE methods.
Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting
This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels.
Cross-Lingual Word Embedding Refinement by $\ell_{1}$ Norm Optimisation
It is therefore recommended that this strategy be adopted as a standard for CLWE methods.
Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis
Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications.
Combining Pre-trained Word Embeddings and Linguistic Features for Sequential Metaphor Identification
The pre-trained word embeddings GloVe, ELMo and BERT have individually shown good performance on sequential metaphor identification.
Interpreting Verbal Metaphors by Paraphrasing
Metaphorical expressions are difficult linguistic phenomena, challenging diverse Natural Language Processing tasks.
Summarising Historical Text in Modern Languages
We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern language.
Generating Descriptions for Sequential Images with Local-Object Attention and Global Semantic Context Modelling
In this paper, we propose an end-to-end CNN-LSTM model for generating descriptions for sequential images with a local-object attention mechanism.
Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation
The Variational Autoencoder (VAE) is a popular and powerful model applied to text modelling to generate diverse sentences.
DGST: a Dual-Generator Network for Text Style Transfer
We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer.
A Text Reassembling Approach to Natural Language Generation
Recent years have seen a number of proposals for performing Natural Language Generation (NLG) based in large part on statistical techniques.
Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition
We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for classification and sequence labelling) to jointly extract dialogue states.
Understanding Linearity of Cross-Lingual Word Embedding Mappings
The technique of Cross-Lingual Word Embedding (CLWE) plays a fundamental role in tackling Natural Language Processing challenges for low-resource languages.
A Stable Variational Autoencoder for Text Modelling
Variational Autoencoder (VAE) is a powerful method for learning representations of high-dimensional data.
Generating Quantified Descriptions of Abstract Visual Scenes
Quantified expressions have always taken up a central position in formal theories of meaning and language use.
QTUNA: A Corpus for Understanding How Speakers Use Quantification
To inform these algorithms, we conducted on a series of elicitation experiments in which human speakers were asked to perform a linguistic task that invites the use of quantified expressions.
Deep Ensemble Learning for News Stance Detection
The second approach is based on word embedding, where word2vec model is introduced and two document similarities calculation algorithms are implemented: wor2vec cosine similarity and WMD distance.
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
We demonstrate the utility of our method for attribute manipulation in autoencoders trained across varied domains, using both human evaluation and automated methods.
Ranked #7 on
Image Generation
on CelebA 256x256
(FID metric)
End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories
End-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification.
SimpleNLG-ZH: a Linguistic Realisation Engine for Mandarin
We introduce SimpleNLG-ZH, a realisation engine for Mandarin that follows the software design paradigm of SimpleNLG (Gatt and Reiter, 2009).
Modelling Pro-drop with the Rational Speech Acts Model
We extend the classic Referring Expressions Generation task by considering zero pronouns in {``}pro-drop{''} languages such as Chinese, modelling their use by means of the Bayesian Rational Speech Acts model (Frank and Goodman, 2012).
A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition.
Ranked #4 on
Dialogue Act Classification
on Switchboard corpus
Word Embedding and WordNet Based Metaphor Identification and Interpretation
Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation.
ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet
This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes.
Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals
We develop a computational model to discover the potential causes of depression by analysing the topics in a usergenerated text.
Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences
Contrastive opinion mining is essential in identifying, extracting and organising opinions from user generated texts.
