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Found 96 papers, 54 papers with code
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.
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.
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.
Structured Information Matters: Incorporating Abstract Meaning Representation into LLMs for Improved Open-Domain Dialogue Evaluation
Trainable evaluation metrics are commonly trained with true positive and randomly selected negative responses, resulting in a tendency for them to assign a higher score to the responses that share higher content similarity with a given context.
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
To bridge this gap, we introduce COIG-CQIA, a high-quality Chinese instruction tuning dataset.
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.
DEEP-ICL: Definition-Enriched Experts for Language Model In-Context Learning
It has long been assumed that the sheer number of parameters in large language models (LLMs) drives in-context learning (ICL) capabilities, enabling remarkable performance improvements by leveraging task-specific 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.
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.
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.
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
In this paper, we propose a novel context-aware graph-attention model (Context-aware GAT), which can effectively incorporate global features of relevant knowledge graphs based on a context-enhanced knowledge aggregation process.
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.
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.
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.
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.
Interpreting Verbal Metaphors by Paraphrasing
Metaphorical expressions are difficult linguistic phenomena, challenging diverse Natural Language Processing tasks.
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.
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.
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.
Generating Quantified Descriptions of Abstract Visual Scenes
Quantified expressions have always taken up a central position in formal theories of meaning and language use.
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.
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).
Statistical NLG for Generating the Content and Form of Referring Expressions
This paper argues that a new generic approach to statistical NLG can be made to perform Referring Expression Generation (REG) successfully.
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).
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.
