no code implementations • EMNLP 2020 • Qianchu Liu, Diana McCarthy, Anna Korhonen
One of the most powerful features of contextualized models is their dynamic embeddings for words in context, leading to state-of-the-art representations for context-aware lexical semantics.
no code implementations • WMT (EMNLP) 2021 • Viktor Hangya, Qianchu Liu, Dario Stojanovski, Alexander Fraser, Anna Korhonen
The performance of NMT systems has improved drastically in the past few years but the translation of multi-sense words still poses a challenge.
no code implementations • 23 Oct 2023 • Qianchu Liu, Stephanie Hyland, Shruthi Bannur, Kenza Bouzid, Daniel C. Castro, Maria Teodora Wetscherek, Robert Tinn, Harshita Sharma, Fernando Pérez-García, Anton Schwaighofer, Pranav Rajpurkar, Sameer Tajdin Khanna, Hoifung Poon, Naoto Usuyama, Anja Thieme, Aditya V. Nori, Matthew P. Lungren, Ozan Oktay, Javier Alvarez-Valle
In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models.
no code implementations • 23 Mar 2023 • Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Sheng Zhang, Tristan Naumann, Aditya Nori, Hoifung Poon, Javier Alvarez-Valle, Ozan Oktay, Stephanie L. Hyland
We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on NLI, text summarisation and embedding learning.
no code implementations • CVPR 2023 • Shruthi Bannur, Stephanie Hyland, Qianchu Liu, Fernando Pérez-García, Maximilian Ilse, Daniel C. Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anja Thieme, Anton Schwaighofer, Maria Wetscherek, Matthew P. Lungren, Aditya Nori, Javier Alvarez-Valle, Ozan Oktay
Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images.
no code implementations • 13 Dec 2021 • Qianchu Liu, Diana McCarthy, Anna Korhonen
Our findings demonstrate that models are usually not being tested for word-in-context semantics in the same way as humans are in these tasks, which helps us better understand the model-human gap.
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 • Qianchu Liu, Edoardo M. Ponti, Diana McCarthy, Ivan Vulić, Anna Korhonen
In order to address these gaps, we present AM2iCo (Adversarial and Multilingual Meaning in Context), a wide-coverage cross-lingual and multilingual evaluation set; it aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts for 14 language pairs.
1 code implementation • EMNLP 2020 • Edoardo Maria Ponti, Goran Glavaš, Olga Majewska, Qianchu Liu, Ivan Vulić, Anna Korhonen
In order to simulate human language capacity, natural language processing systems must be able to reason about the dynamics of everyday situations, including their possible causes and effects.
Ranked #3 on Cross-Lingual Transfer on XCOPA (using extra training data)
no code implementations • CONLL 2019 • Qianchu Liu, Diana McCarthy, Ivan Vuli{\'c}, Anna Korhonen
In this paper, we present a thorough investigation on methods that align pre-trained contextualized embeddings into shared cross-lingual context-aware embedding space, providing strong reference benchmarks for future context-aware crosslingual models.
no code implementations • SEMEVAL 2019 • Qianchu Liu, Diana McCarthy, Anna Korhonen
There is a growing awareness of the need to handle rare and unseen words in word representation modelling.