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.
1 code implementation • ACL 2022 • Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier
Research in stance detection has so far focused on models which leverage purely textual input.
no code implementations • EACL (Hackashop) 2021 • Costanza Conforti, Jakob Berndt, Marco Basaldella, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier
Cross-target generalization constitutes an important issue for news Stance Detection (SD).
no code implementations • EACL (WASSA) 2021 • Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier
Cross-target generalization is a known problem in stance detection (SD), where systems tend to perform poorly when exposed to targets unseen during training.
1 code implementation • 25 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.
no code implementations • 16 Feb 2024 • Tiancheng Hu, Nigel Collier
However, when the utility of persona variables is low (i. e., explaining <10\% of human annotations), persona prompting has little effect.
no code implementations • 15 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.
1 code implementation • 15 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.
no code implementations • 19 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.
no code implementations • 24 Oct 2023 • Tiancheng Hu, Yara Kyrychenko, Steve Rathje, Nigel Collier, Sander van der Linden, Jon Roozenbeek
In this study, we investigate whether ingroup solidarity and outgroup hostility, fundamental social biases known from social science, are present in 51 large language models.
1 code implementation • 20 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.
no code implementations • 9 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.
Ranked #7 on Question Answering on Bamboogle
1 code implementation • 31 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.
1 code implementation • 23 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.
1 code implementation • 22 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.
1 code implementation • 21 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.
no code implementations • 8 Apr 2023 • Zihao Fu, Wai Lam, Qian Yu, Anthony Man-Cho So, Shengding Hu, Zhiyuan Liu, Nigel Collier
Grounded on our analysis, we propose a novel partial attention language model to solve the attention degeneration problem.
no code implementations • 25 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.
no code implementations • 24 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.
1 code implementation • 20 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
1 code implementation • 19 Dec 2022 • Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos
Visual language data such as plots, charts, and infographics are ubiquitous in the human world.
Ranked #1 on Visual Question Answering on PlotQA-D2
no code implementations • 9 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.
1 code implementation • 28 Nov 2022 • Zihao Fu, Haoran Yang, Anthony Man-Cho So, Wai Lam, Lidong Bing, Nigel Collier
How to choose the tunable parameters?
2 code implementations • 25 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.
1 code implementation • 29 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.
no code implementations • 26 Sep 2022 • Fangyu Liu, Julian Martin Eisenschlos, Jeremy R. Cole, Nigel Collier
Language models (LMs) trained on raw texts have no direct access to the physical world.
1 code implementation • 5 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.
2 code implementations • 30 Apr 2022 • Fangyu Liu, Guy Emerson, Nigel Collier
Spatial relations are a basic part of human cognition.
Ranked #1 on Visual Reasoning on VSR
no code implementations • 30 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.
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.
2 code implementations • 13 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.
2 code implementations • Findings (NAACL) 2022 • Yixuan Su, Fangyu Liu, Zaiqiao Meng, Tian Lan, Lei Shu, Ehsan Shareghi, Nigel Collier
Masked language models (MLMs) such as BERT and RoBERTa have revolutionized the field of Natural Language Understanding in the past few years.
1 code implementation • ACL 2022 • Wenxuan Zhou, Fangyu Liu, Ivan Vulić, Nigel Collier, Muhao Chen
To achieve this, it is crucial to represent multilingual knowledge in a shared/unified space.
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.
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.
Ranked #1 on Zero-Shot Cross-Lingual Transfer on MaRVL
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.
1 code implementation • EMNLP 2021 • Zaiqiao Meng, Fangyu Liu, Thomas Hikaru Clark, Ehsan Shareghi, Nigel Collier
Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks.
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.
1 code implementation • Findings (EMNLP) 2021 • Yixuan Su, Zaiqiao Meng, Simon Baker, Nigel Collier
Neural table-to-text generation models have achieved remarkable progress on an array of tasks.
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.
1 code implementation • EMNLP 2021 • Fangyu Liu, Ivan Vulić, Anna Korhonen, Nigel Collier
In this work, we demonstrate that it is possible to turn MLMs into effective universal lexical and sentence encoders even without any additional data and without any supervision.
Ranked #15 on Semantic Textual Similarity on STS16
Contrastive Learning Cross-Lingual Semantic Textual Similarity +5
1 code implementation • EACL 2021 • Yixuan Su, Deng Cai, Yan Wang, David Vandyke, Simon Baker, Piji Li, Nigel Collier
In this work, we show that BERT can be employed as the backbone of a NAG model to greatly improve performance.
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.
Ranked #3 on Conversational Response Selection on RRS
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier
We present a new challenging news dataset that targets both stance detection (SD) and fine-grained evidence retrieval (ER).
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.
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.
2 code implementations • 28 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)
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.
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.
no code implementations • 5 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).
no code implementations • 5 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.
no code implementations • 21 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.
no code implementations • 21 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.
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.
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.
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.
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.
no code implementations • 12 Nov 2018 • Victor Prokhorov, Mohammad Taher Pilehvar, Dimitri Kartsaklis, Pietro Lio, Nigel Collier
Word embedding techniques heavily rely on the abundance of training data for individual words.
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.
1 code implementation • 29 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.
2 code implementations • EMNLP 2018 • Hoang-Quynh Le, Duy-Cat Can, Sinh T. Vu, Thanh Hai Dang, Mohammad Taher Pilehvar, Nigel Collier
Experimental performance on the task of relation classification has generally improved using deep neural network architectures.
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.
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.
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.
1 code implementation • ACL 2017 • Mohammad Taher Pilehvar, Jose Camacho-Collados, Roberto Navigli, Nigel Collier
Lexical ambiguity can impede NLP systems from accurate understanding of semantics.
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.
no code implementations • 24 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.
1 code implementation • ACL 2017 • Milan Gritta, Mohammad Taher Pilehvar, Nut Limsopatham, Nigel Collier
Named entities are frequently used in a metonymic manner.
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.
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.
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).
Ranked #6 on Named Entity Recognition (NER) on WNUT 2016
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.
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.
1 code implementation • 2 Mar 2012 • Song Liu, Makoto Yamada, Nigel Collier, Masashi Sugiyama
The objective of change-point detection is to discover abrupt property changes lying behind time-series data.