5 citations · 22 across the 20 of their papers we have counts for
25 papers
A Simple Data Augmentation Strategy for Text-in-Image Scientific VQA
Belal Shoer, Yova Kementchedjhieva
Scientific visual question answering poses significant challenges for vision-language models due to the complexity of scientific figures and their multimodal context. Traditional a…
Noise is an Efficient Learner for Zero-Shot Vision-Language Models
Raza Imam, Asif Hanif, Jian Zhang +3
Recently, test-time adaptation has garnered attention as a method for tuning models without labeled data. The conventional modus operandi for adapting pre-trained vision-language m…
EFSA: Episodic Few-Shot Adaptation for Text-to-Image Retrieval
Muhammad Huzaifa, Yova Kementchedjhieva
Text-to-image retrieval is a critical task for managing diverse visual content, but common benchmarks for the task rely on small, single-domain datasets that fail to capture real-w…
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo +73
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge pres…
Multimodal Large Language Models to Support Real-World Fact-Checking
Jiahui Geng, Yova Kementchedjhieva, Preslav Nakov +1
Multimodal large language models (MLLMs) carry the potential to support humans in processing vast amounts of information. While MLLMs are already being used as a fact-checking tool…
MuLan: A Study of Fact Mutability in Language Models
Constanza Fierro, Nicolas Garneau, Emanuele Bugliarello +2
Facts are subject to contingencies and can be true or false in different circumstances. One such contingency is time, wherein some facts mutate over a given period, e.g., the presi…
Answerability in Retrieval-Augmented Open-Domain Question Answering
Rustam Abdumalikov, Pasquale Minervini, Yova Kementchedjhieva
The performance of Open-Domain Question Answering (ODQA) retrieval systems can exhibit sub-optimal behavior, providing text excerpts with varying degrees of irrelevance. Unfortunat…
Cultural Adaptation of Recipes
Yong Cao, Yova Kementchedjhieva, Ruixiang Cui +5
Building upon the considerable advances in Large Language Models (LLMs), we are now equipped to address more sophisticated tasks demanding a nuanced understanding of cross-cultural…
Structural Similarities Between Language Models and Neural Response Measurements
Jiaang Li, Antonia Karamolegkou, Yova Kementchedjhieva +3
Large language models (LLMs) have complicated internal dynamics, but induce representations of words and phrases whose geometry we can study. Human language processing is also opaq…
Retrieval-augmented Multi-label Text Classification
Ilias Chalkidis, Yova Kementchedjhieva
Multi-label text classification (MLC) is a challenging task in settings of large label sets, where label support follows a Zipfian distribution. In this paper, we address this prob…
An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text
Yova Kementchedjhieva, Ilias Chalkidis
Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other cl…
Do Vision and Language Models Share Concepts? A Vector Space Alignment Study
Jiaang Li, Yova Kementchedjhieva, Constanza Fierro +1
Large-scale pretrained language models (LMs) are said to ``lack the ability to connect utterances to the world'' (Bender and Koller, 2020), because they do not have ``mental models…
SmallCap: Lightweight Image Captioning Prompted with Retrieval Augmentation
Rita Ramos, Bruno Martins, Desmond Elliott +1
Recent advances in image captioning have focused on scaling the data and model size, substantially increasing the cost of pre-training and finetuning. As an alternative to large mo…
Dynamic Forecasting of Conversation Derailment
Yova Kementchedjhieva, Anders Søgaard
Online conversations can sometimes take a turn for the worse, either due to systematic cultural differences, accidental misunderstandings, or mere malice. Automatically forecasting…
John praised Mary because he? Implicit Causality Bias and Its Interaction with Explicit Cues in LMs
Yova Kementchedjhieva, Mark Anderson, Anders Søgaard
Some interpersonal verbs can implicitly attribute causality to either their subject or their object and are therefore said to carry an implicit causality (IC) bias. Through this bi…
The ApposCorpus: A new multilingual, multi-domain dataset for factual appositive generation
Yova Kementchedjhieva, Di Lu, Joel Tetreault
News articles, image captions, product reviews and many other texts mention people and organizations whose name recognition could vary for different audiences. In such cases, backg…
PuzzLing Machines: A Challenge on Learning From Small Data
Gözde Gül Şahin, Yova Kementchedjhieva, Phillip Rust +1
Deep neural models have repeatedly proved excellent at memorizing surface patterns from large datasets for various ML and NLP benchmarks. They struggle to achieve human-like thinki…
How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?
Hila Gonen, Yova Kementchedjhieva, Yoav Goldberg
Many natural languages assign grammatical gender also to inanimate nouns in the language. In such languages, words that relate to the gender-marked nouns are inflected to agree wit…
Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary Induction
Yova Kementchedjhieva, Mareike Hartmann, Anders Søgaard
The task of bilingual dictionary induction (BDI) is commonly used for intrinsic evaluation of cross-lingual word embeddings. The largest dataset for BDI was generated automatically…
Uncovering Probabilistic Implications in Typological Knowledge Bases
Johannes Bjerva, Yova Kementchedjhieva, Ryan Cotterell +1
The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post…
A Probabilistic Generative Model of Linguistic Typology
Johannes Bjerva, Yova Kementchedjhieva, Ryan Cotterell +1
In the principles-and-parameters framework, the structural features of languages depend on parameters that may be toggled on or off, with a single parameter often dictating the sta…
Copenhagen at CoNLL--SIGMORPHON 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding
Yova Kementchedjhieva, Johannes Bjerva, Isabelle Augenstein
This paper documents the Team Copenhagen system which placed first in the CoNLL--SIGMORPHON 2018 shared task on universal morphological reinflection, Task 2 with an overall accurac…
Why is unsupervised alignment of English embeddings from different algorithms so hard?
Mareike Hartmann, Yova Kementchedjhieva, Anders Søgaard
This paper presents a challenge to the community: Generative adversarial networks (GANs) can perfectly align independent English word embeddings induced using the same algorithm, b…
Indicatements that character language models learn English morpho-syntactic units and regularities
Yova Kementchedjhieva, Adam Lopez
Character language models have access to surface morphological patterns, but it is not clear whether or how they learn abstract morphological regularities. We instrument a characte…