Search Results for author: Nigel Collier

Found 90 papers, 51 papers with code

Integrating Transformers and Knowledge Graphs for Twitter Stance Detection

no code implementations WNUT (ACL) 2021 Thomas Clark, Costanza Conforti, Fangyu Liu, Zaiqiao Meng, Ehsan Shareghi, Nigel Collier

Stance detection (SD) entails classifying the sentiment of a text towards a given target, and is a relevant sub-task for opinion mining and social media analysis.

Knowledge Graphs Knowledge Probing +3

Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments

2 code implementations17 Jun 2024 Han Zhou, Xingchen Wan, Yinhong Liu, Nigel Collier, Ivan Vulić, Anna Korhonen

Motivated by this phenomenon, we propose an automatic Zero-shot Evaluation-oriented Prompt Optimization framework, ZEPO, which aims to produce fairer preference decisions and improve the alignment of LLM evaluators with human judgments.

Fairness Language Modelling +3

Can LLM be a Personalized Judge?

1 code implementation17 Jun 2024 Yijiang River Dong, Tiancheng Hu, Nigel Collier

Ensuring that large language models (LLMs) reflect diverse user values and preferences is crucial as their user bases expand globally.

TopViewRS: Vision-Language Models as Top-View Spatial Reasoners

1 code implementation4 Jun 2024 Chengzu Li, Caiqi Zhang, Han Zhou, Nigel Collier, Anna Korhonen, Ivan Vulić

In this work, we thus study their capability to understand and reason over spatial relations from the top view.

Multiple-choice

LUQ: Long-text Uncertainty Quantification for LLMs

no code implementations29 Mar 2024 Caiqi Zhang, Fangyu Liu, Marco Basaldella, Nigel Collier

Our study first highlights the limitations of current UQ methods in handling long text generation.

Text Generation Uncertainty Quantification

Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators

1 code implementation25 Mar 2024 Yinhong Liu, Han Zhou, Zhijiang Guo, Ehsan Shareghi, Ivan Vulić, Anna Korhonen, Nigel Collier

Large Language Models (LLMs) have demonstrated promising capabilities as automatic evaluators in assessing the quality of generated natural language.

Language Modelling Large Language Model

Quantifying the Persona Effect in LLM Simulations

no code implementations16 Feb 2024 Tiancheng Hu, Nigel Collier

Notably, we find a linear relationship in our setting: the stronger the correlation between persona variables and human annotations, the more accurate the LLM predictions are using persona prompting.

TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles

no code implementations15 Feb 2024 Yinhong Liu, Yimai Fang, David Vandyke, Nigel Collier

In light of recent advances in large language models (LLMs), the expectations for the next generation of virtual assistants include enhanced naturalness and adaptability across diverse usage scenarios.

Response Generation

Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherence

1 code implementation15 Feb 2024 Yinhong Liu, Yixuan Su, Ehsan Shareghi, Nigel Collier

Recent large language models (LLMs) have shown remarkable performance in aligning generated text with user intentions across various tasks.

Coherence Evaluation Text Generation

Instruct-SCTG: Guiding Sequential Controlled Text Generation through Instructions

no code implementations19 Dec 2023 Yinhong Liu, Yixuan Su, Ehsan Shareghi, Nigel Collier

Instruction-tuned large language models have shown remarkable performance in aligning generated text with user intentions across various tasks.

Text Generation

Generative Language Models Exhibit Social Identity Biases

no code implementations24 Oct 2023 Tiancheng Hu, Yara Kyrychenko, Steve Rathje, Nigel Collier, Sander van der Linden, Jon Roozenbeek

The surge in popularity of large language models has given rise to concerns about biases that these models could learn from humans.

POSQA: Probe the World Models of LLMs with Size Comparisons

1 code implementation20 Oct 2023 Chang Shu, Jiuzhou Han, Fangyu Liu, Ehsan Shareghi, Nigel Collier

Embodied language comprehension emphasizes that language understanding is not solely a matter of mental processing in the brain but also involves interactions with the physical and social environment.

Question Answering

FireAct: Toward Language Agent Fine-tuning

no code implementations9 Oct 2023 Baian Chen, Chang Shu, Ehsan Shareghi, Nigel Collier, Karthik Narasimhan, Shunyu Yao

Recent efforts have augmented language models (LMs) with external tools or environments, leading to the development of language agents that can reason and act.

Question Answering

Sparkles: Unlocking Chats Across Multiple Images for Multimodal Instruction-Following Models

1 code implementation31 Aug 2023 Yupan Huang, Zaiqiao Meng, Fangyu Liu, Yixuan Su, Nigel Collier, Yutong Lu

Our experiments validate the effectiveness of SparklesChat in understanding and reasoning across multiple images and dialogue turns.

Instruction Following Visual Reasoning

BAND: Biomedical Alert News Dataset

1 code implementation23 May 2023 Zihao Fu, Meiru Zhang, Zaiqiao Meng, Yannan Shen, David Buckeridge, Nigel Collier

Infectious disease outbreaks continue to pose a significant threat to human health and well-being.

Epidemiology named-entity-recognition +3

Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization

1 code implementation22 May 2023 Zihao Fu, Yixuan Su, Zaiqiao Meng, Nigel Collier

To alleviate the need of human effort, dictionary-based approaches have been proposed to extract named entities simply based on a given dictionary.

named-entity-recognition Named Entity Recognition

PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs

1 code implementation21 May 2023 Jiuzhou Han, Nigel Collier, Wray Buntine, Ehsan Shareghi

We show how a small language model could be trained to act as a verifier module for the output of an LLM~(i. e., ChatGPT, GPT-4), and to iteratively improve its performance via fine-grained corrective instructions.

Data Augmentation Graph Generation +1

COFFEE: A Contrastive Oracle-Free Framework for Event Extraction

1 code implementation25 Mar 2023 Meiru Zhang, Yixuan Su, Zaiqiao Meng, Zihao Fu, Nigel Collier

In this study, we consider a more realistic setting of this task, namely the Oracle-Free Event Extraction (OFEE) task, where only the input context is given without any oracle information, including event type, event ontology and trigger word.

Event Extraction

A Stability Analysis of Fine-Tuning a Pre-Trained Model

no code implementations24 Jan 2023 Zihao Fu, Anthony Man-Cho So, Nigel Collier

The theoretical bounds explain why and how several existing methods can stabilize the fine-tuning procedure.

DePlot: One-shot visual language reasoning by plot-to-table translation

1 code implementation20 Dec 2022 Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun

Compared with a SOTA model finetuned on more than >28k data points, DePlot+LLM with just one-shot prompting achieves a 24. 0% improvement over finetuned SOTA on human-written queries from the task of chart QA.

Chart Question Answering Factual Inconsistency Detection in Chart Captioning +3

Plug-and-Play Recipe Generation with Content Planning

no code implementations9 Dec 2022 Yinhong Liu, Yixuan Su, Ehsan Shareghi, Nigel Collier

Specifically, it optimizes the joint distribution of the natural language sequence and the global content plan in a plug-and-play manner.

Recipe Generation Sentence +1

Contrastive Search Is What You Need For Neural Text Generation

2 code implementations25 Oct 2022 Yixuan Su, Nigel Collier

Based on our findings, we further assess the contrastive search decoding method using off-the-shelf LMs on four generation tasks across 16 languages.

Contrastive Learning Language Modelling +1

How to tackle an emerging topic? Combining strong and weak labels for Covid news NER

1 code implementation29 Sep 2022 Aleksander Ficek, Fangyu Liu, Nigel Collier

Being able to train Named Entity Recognition (NER) models for emerging topics is crucial for many real-world applications especially in the medical domain where new topics are continuously evolving out of the scope of existing models and datasets.

named-entity-recognition Named Entity Recognition +2

Language Models Can See: Plugging Visual Controls in Text Generation

1 code implementation5 May 2022 Yixuan Su, Tian Lan, Yahui Liu, Fangyu Liu, Dani Yogatama, Yan Wang, Lingpeng Kong, Nigel Collier

MAGIC is a flexible framework and is theoretically compatible with any text generation tasks that incorporate image grounding.

Image Captioning Image-text matching +3

Probing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders

no code implementations30 Apr 2022 Ivan Vulić, Goran Glavaš, Fangyu Liu, Nigel Collier, Edoardo Maria Ponti, Anna Korhonen

In this work, we probe SEs for the amount of cross-lingual lexical knowledge stored in their parameters, and compare them against the original multilingual LMs.

Contrastive Learning Cross-Lingual Entity Linking +6

Improving Word Translation via Two-Stage Contrastive Learning

1 code implementation ACL 2022 Yaoyiran Li, Fangyu Liu, Nigel Collier, Anna Korhonen, Ivan Vulić

At Stage C1, we propose to refine standard cross-lingual linear maps between static word embeddings (WEs) via a contrastive learning objective; we also show how to integrate it into the self-learning procedure for even more refined cross-lingual maps.

Bilingual Lexicon Induction Contrastive Learning +7

A Contrastive Framework for Neural Text Generation

2 code implementations13 Feb 2022 Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, Nigel Collier

Text generation is of great importance to many natural language processing applications.

Diversity Text Generation

Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models

1 code implementation ACL 2022 Zaiqiao Meng, Fangyu Liu, Ehsan Shareghi, Yixuan Su, Charlotte Collins, Nigel Collier

To catalyse the research in this direction, we release a well-curated biomedical knowledge probing benchmark, MedLAMA, which is constructed based on the Unified Medical Language System (UMLS) Metathesaurus.

Knowledge Probing Transfer Learning

Visually Grounded Reasoning across Languages and Cultures

3 code implementations EMNLP 2021 Fangyu Liu, Emanuele Bugliarello, Edoardo Maria Ponti, Siva Reddy, Nigel Collier, Desmond Elliott

The design of widespread vision-and-language datasets and pre-trained encoders directly adopts, or draws inspiration from, the concepts and images of ImageNet.

Visual Reasoning Zero-Shot Learning

MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models

1 code implementation CoNLL (EMNLP) 2021 Qianchu Liu, Fangyu Liu, Nigel Collier, Anna Korhonen, Ivan Vulić

Recent work indicated that pretrained language models (PLMs) such as BERT and RoBERTa can be transformed into effective sentence and word encoders even via simple self-supervised techniques.

Contextualised Word Representations Contrastive Learning +1

Plan-then-Generate: Controlled Data-to-Text Generation via Planning

2 code implementations Findings (EMNLP) 2021 Yixuan Su, David Vandyke, Sihui Wang, Yimai Fang, Nigel Collier

However, the lack of ability of neural models to control the structure of generated output can be limiting in certain real-world applications.

Data-to-Text Generation Diversity +1

Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking

1 code implementation ACL 2021 Fangyu Liu, Ivan Vulić, Anna Korhonen, Nigel Collier

To this end, we propose and evaluate a series of cross-lingual transfer methods for the XL-BEL task, and demonstrate that general-domain bitext helps propagate the available English knowledge to languages with little to no in-domain data.

Cross-Lingual Transfer Entity Linking

Dialogue Response Selection with Hierarchical Curriculum Learning

1 code implementation ACL 2021 Yixuan Su, Deng Cai, Qingyu Zhou, Zibo Lin, Simon Baker, Yunbo Cao, Shuming Shi, Nigel Collier, Yan Wang

As for IC, it progressively strengthens the model's ability in identifying the mismatching information between the dialogue context and a response candidate.

Conversational Response Selection

Self-Alignment Pretraining for Biomedical Entity Representations

1 code implementation NAACL 2021 Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng, Marco Basaldella, Nigel Collier

Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge.

Benchmarking Entity Linking +2

COMETA: A Corpus for Medical Entity Linking in the Social Media

1 code implementation EMNLP 2020 Marco Basaldella, Fangyu Liu, Ehsan Shareghi, Nigel Collier

Whilst there has been growing progress in Entity Linking (EL) for general language, existing datasets fail to address the complex nature of health terminology in layman's language.

Diversity Entity Linking

Visual Pivoting for (Unsupervised) Entity Alignment

2 code implementations28 Sep 2020 Fangyu Liu, Muhao Chen, Dan Roth, Nigel Collier

This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs).

Ranked #3 on Multi-modal Entity Alignment on UMVM-oea-d-w-v1 (using extra training data)

Knowledge Graphs Multi-modal Entity Alignment

Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders

1 code implementation ACL (RepL4NLP) 2021 Victor Prokhorov, Yingzhen Li, Ehsan Shareghi, Nigel Collier

It has been long known that sparsity is an effective inductive bias for learning efficient representation of data in vectors with fixed dimensionality, and it has been explored in many areas of representation learning.

Inductive Bias Representation Learning +3

Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter

2 code implementations ACL 2020 Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier

We present a new challenging stance detection dataset, called Will-They-Won't-They (WT-WT), which contains 51, 284 tweets in English, making it by far the largest available dataset of the type.

Stance Detection

Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory

no code implementations5 Apr 2020 Yixuan Su, Yan Wang, Simon Baker, Deng Cai, Xiaojiang Liu, Anna Korhonen, Nigel Collier

A stylistic response generator then takes the prototype and the desired language style as model input to obtain a high-quality and stylistic response.

Dialogue Generation Information Retrieval +1

Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy

no code implementations5 Apr 2020 Yixuan Su, Deng Cai, Yan Wang, Simon Baker, Anna Korhonen, Nigel Collier, Xiaojiang Liu

To enable better balance between the content quality and the style, we introduce a new training strategy, know as Information-Guided Reinforcement Learning (IG-RL).

Dialogue Generation reinforcement-learning +2

Global Health Monitor: A Web-based System for Detecting and Mapping Infectious Diseases

no code implementations21 Nov 2019 Son Doan, Quoc-Hung Ngo, Ai Kawazoe, Nigel Collier

We present the Global Health Monitor, an online Web-based system for detecting and mapping infectious disease outbreaks that appear in news stories.

named-entity-recognition Named Entity Recognition +2

An Empirical Study of Sections in Classifying Disease Outbreak Reports

no code implementations21 Nov 2019 Son Doan, Mike Conway, Nigel Collier

Identifying articles that relate to infectious diseases is a necessary step for any automatic bio-surveillance system that monitors news articles from the Internet.

General Classification Sentence +2

BioReddit: Word Embeddings for User-Generated Biomedical NLP

no code implementations WS 2019 Marco Basaldella, Nigel Collier

Word embeddings, in their different shapes and iterations, have changed the natural language processing research landscape in the last years.

Word Embeddings

On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation

1 code implementation WS 2019 Victor Prokhorov, Ehsan Shareghi, Yingzhen Li, Mohammad Taher Pilehvar, Nigel Collier

While the explicit constraint naturally avoids posterior collapse, we use it to further understand the significance of the KL term in controlling the information transmitted through the VAE channel.

Text Generation

A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction

1 code implementation NAACL 2019 Duy-Cat Can, Hoang-Quynh Le, Quang-Thuy Ha, Nigel Collier

To extract the relationship between two entities in a sentence, two common approaches are (1) using their shortest dependency path (SDP) and (2) using an attention model to capture a context-based representation of the sentence.

Relation Relation Extraction +1

Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models

1 code implementation NAACL 2019 Victor Prokhorov, Mohammad Taher Pilehvar, Nigel Collier

We present a novel method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem.

Decoder

Towards Automatic Fake News Detection: Cross-Level Stance Detection in News Articles

no code implementations WS 2018 Costanza Conforti, Mohammad Taher Pilehvar, Nigel Collier

In this paper, we propose to adapt the four-staged pipeline proposed by Zubiaga et al. (2018) for the Rumor Verification task to the problem of Fake News Detection.

Fake News Detection Stance Detection

A Pragmatic Guide to Geoparsing Evaluation

1 code implementation29 Oct 2018 Milan Gritta, Mohammad Taher Pilehvar, Nigel Collier

Empirical methods in geoparsing have thus far lacked a standard evaluation framework describing the task, metrics and data used to compare state-of-the-art systems.

named-entity-recognition Named Entity Recognition +2

Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models

no code implementations EMNLP 2018 Mohammad Taher Pilehvar, Dimitri Kartsaklis, Victor Prokhorov, Nigel Collier

Rare word representation has recently enjoyed a surge of interest, owing to the crucial role that effective handling of infrequent words can play in accurate semantic understanding.

Word Embeddings Word Similarity

Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs

no code implementations EMNLP 2018 Dimitri Kartsaklis, Mohammad Taher Pilehvar, Nigel Collier

Further, the knowledge base space is prepared by collecting random walks from a graph enhanced with textual features, which act as a set of semantic bridges between text and knowledge base entities.

General Classification Sentence +1

Which Melbourne? Augmenting Geocoding with Maps

no code implementations ACL 2018 Milan Gritta, Mohammad Taher Pilehvar, Nigel Collier

The purpose of text geolocation is to associate geographic information contained in a document with a set (or sets) of coordinates, either implicitly by using linguistic features and/or explicitly by using geographic metadata combined with heuristics.

SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity

no code implementations SEMEVAL 2017 Jose Camacho-Collados, Mohammad Taher Pilehvar, Nigel Collier, Roberto Navigli

This paper introduces a new task on Multilingual and Cross-lingual SemanticThis paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word pairs within and across five languages: English, Farsi, German, Italian and Spanish.

Information Retrieval Machine Translation +9

Learning Rare Word Representations using Semantic Bridging

no code implementations24 Jul 2017 Victor Prokhorov, Mohammad Taher Pilehvar, Dimitri Kartsaklis, Pietro Lió, Nigel Collier

We propose a methodology that adapts graph embedding techniques (DeepWalk (Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as cross-lingual vector space mapping approaches (Least Squares and Canonical Correlation Analysis) in order to merge the corpus and ontological sources of lexical knowledge.

Graph Embedding Word Similarity

Inducing Embeddings for Rare and Unseen Words by Leveraging Lexical Resources

no code implementations EACL 2017 Mohammad Taher Pilehvar, Nigel Collier

We put forward an approach that exploits the knowledge encoded in lexical resources in order to induce representations for words that were not encountered frequently during training.

Word Embeddings

Bidirectional LSTM for Named Entity Recognition in Twitter Messages

no code implementations WS 2016 Nut Limsopatham, Nigel Collier

In this paper, we present our approach for named entity recognition in Twitter messages that we used in our participation in the Named Entity Recognition in Twitter shared task at the COLING 2016 Workshop on Noisy User-generated text (WNUT).

Feature Engineering named-entity-recognition +3

Learning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition

no code implementations WS 2016 Nut Limsopatham, Nigel Collier

End-to-end neural network models for named entity recognition (NER) have shown to achieve effective performances on general domain datasets (e. g. newswire), without requiring additional hand-crafted features.

Feature Engineering named-entity-recognition +3

De-Conflated Semantic Representations

1 code implementation EMNLP 2016 Mohammad Taher Pilehvar, Nigel Collier

One major deficiency of most semantic representation techniques is that they usually model a word type as a single point in the semantic space, hence conflating all the meanings that the word can have.

Semantic Similarity Semantic Textual Similarity

Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages

no code implementations EMNLP 2015 Nut Limsopatham, Nigel Collier

Previous studies have shown that health reports in social media, such as DailyStrength and Twitter, have potential for monitoring health conditions (e. g. adverse drug reactions, infectious diseases) in particular communities.

Machine Translation Translation

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