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Found 12 papers, 3 papers with code
Towards Better Context-aware Lexical Semantics:Adjusting Contextualized Representations through Static Anchors
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.
Improving Machine Translation of Rare and Unseen Word Senses
The performance of NMT systems has improved drastically in the past few years but the translation of multi-sense words still poses a challenge.
Exploring the Boundaries of GPT-4 in Radiology
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.
Compositional Zero-Shot Domain Transfer with Text-to-Text Models
We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on NLI, text summarisation and embedding learning.
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
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.
Measuring Context-Word Biases in Lexical Semantic Datasets
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.
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models
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.
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples
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.
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
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)
Investigating Cross-Lingual Alignment Methods for Contextualized Embeddings with Token-Level Evaluation
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.
Second-order contexts from lexical substitutes for few-shot learning of word representations
There is a growing awareness of the need to handle rare and unseen words in word representation modelling.
