1 code implementation • ACL 2022 • Lan Zhang, Wray Buntine, Ehsan Shareghi
Injecting desired geometric properties into text representations has attracted a lot of attention.
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 • Dongwon Ryu, Ehsan Shareghi, Meng Fang, Yunqiu Xu, Shirui Pan, Reza Haf
Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces.
1 code implementation • 24 May 2023 • Yuan Yang, Siheng Xiong, Ali Payani, Ehsan Shareghi, Faramarz Fekri
Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature.
1 code implementation • 21 May 2023 • Jiuzhou Han, Nigel Collier, Wray Buntine, Ehsan Shareghi
Large language models (LLMs) have shown great abilities of solving various natural language tasks in different domains.
no code implementations • 6 May 2023 • Dongwon Kelvin Ryu, Meng Fang, Shirui Pan, Gholamreza Haffari, Ehsan Shareghi
Text-based games (TGs) are language-based interactive environments for reinforcement learning.
no code implementations • 26 Mar 2023 • Thuy-Trang Vu, Xuanli He, Gholamreza Haffari, Ehsan Shareghi
In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour.
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 • 24 Oct 2022 • Hao Yang, Jinming Zhao, Gholamreza Haffari, Ehsan Shareghi
Pre-trained speech Transformers have facilitated great success across various speech processing tasks.
1 code implementation • 19 Oct 2022 • Jiuzhou Han, Ehsan Shareghi
Large-scale pre-trained language models (PLMs) have advanced Graph-to-Text (G2T) generation by processing the linearised version of a graph.
no code implementations • 16 Oct 2022 • Jinming Zhao, Hao Yang, Gholamreza Haffari, Ehsan Shareghi
Pre-trained speech Transformers in speech translation (ST) have facilitated state-of-the-art (SotA) results; yet, using such encoders is computationally expensive.
1 code implementation • 15 Oct 2022 • Jinming Zhao, Gholamreza Haffar, Ehsan Shareghi
Training end-to-end speech translation (ST) systems requires sufficiently large-scale data, which is unavailable for most language pairs and domains.
no code implementations • 25 Aug 2022 • Nigel H. Collier, Fangyu Liu, Ehsan Shareghi
Recent advancements in Large Language Models (LLMs) harness linguistic associations in vast natural language data for practical applications.
1 code implementation • 3 Jul 2022 • Jinming Zhao, Hao Yang, Ehsan Shareghi, Gholamreza Haffari
End-to-end speech-to-text translation models are often initialized with pre-trained speech encoder and pre-trained text decoder.
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 • 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.
1 code implementation • 14 Oct 2021 • Lan Zhang, Wray Buntine, Ehsan Shareghi
Deep generative models have been widely used in several areas of NLP, and various techniques have been proposed to augment them or address their training challenges.
1 code implementation • EMNLP 2021 • Jinming Zhao, Philip Arthur, Gholamreza Haffari, Trevor Cohn, Ehsan Shareghi
Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora.
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.
1 code implementation • ACL (RepL4NLP) 2021 • Lan Zhang, Victor Prokhorov, Ehsan Shareghi
To highlight the challenges of achieving representation disentanglement for text domain in an unsupervised setting, in this paper we select a representative set of successfully applied models from the image domain.
1 code implementation • EACL 2021 • Yi Zhu, Ehsan Shareghi, Yingzhen Li, Roi Reichart, Anna Korhonen
Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP.
no code implementations • ACL 2021 • Mengjie Zhao, Yi Zhu, Ehsan Shareghi, Ivan Vulić, Roi Reichart, Anna Korhonen, Hinrich Schütze
Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT.
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.
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.
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.
no code implementations • NAACL 2019 • Ehsan Shareghi, Yingzhen Li, Yi Zhu, Roi Reichart, Anna Korhonen
While neural dependency parsers provide state-of-the-art accuracy for several languages, they still rely on large amounts of costly labeled training data.
no code implementations • NAACL 2019 • Ehsan Shareghi, Daniela Gerz, Ivan Vuli{\'c}, Anna Korhonen
In recent years neural language models (LMs) have set the state-of-the-art performance for several benchmarking datasets.
1 code implementation • TACL 2016 • Ehsan Shareghi, Matthias Petri, Gholamreza Haffari, Trevor Cohn
Efficient methods for storing and querying are critical for scaling high-order n-gram language models to large corpora.
no code implementations • 9 Mar 2015 • Ehsan Shareghi, Gholamreza Haffari, Trevor Cohn, Ann Nicholson
Linguistic structures exhibit a rich array of global phenomena, however commonly used Markov models are unable to adequately describe these phenomena due to their strong locality assumptions.