212 citations · 741 across the 81 of their papers we have counts for
89 papers
Less Stress, More Privacy: Stress Detection on Anonymized Speech of Air Traffic Controllers
Janaki Viswanathan, Alexander Blatt, Konrad Hagemann +1
Air traffic control (ATC) demands multi-tasking under time pressure with high consequences of an error. This can induce stress. Detecting stress is a key point in maintaining the h…
Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead
Jesujoba O. Alabi, Michael A. Hedderich, David Ifeoluwa Adelani +1
With over 2,000 languages and potentially millions of speakers, Africa represents one of the richest linguistic regions in the world. Yet, this diversity is scarcely reflected in s…
Uhura: A Benchmark for Evaluating Scientific Question Answering and Truthfulness in Low-Resource African Languages
Edward Bayes, Israel Abebe Azime, Jesujoba O. Alabi +11
Evaluations of Large Language Models (LLMs) on knowledge-intensive tasks and factual accuracy often focus on high-resource languages primarily because datasets for low-resource lan…
ProverbEval: Exploring LLM Evaluation Challenges for Low-resource Language Understanding
Israel Abebe Azime, Atnafu Lambebo Tonja, Tadesse Destaw Belay +11
With the rapid development of evaluation datasets to assess LLMs understanding across a wide range of subjects and domains, identifying a suitable language understanding benchmark…
AfriHuBERT: A self-supervised speech representation model for African languages
Jesujoba O. Alabi, Xuechen Liu, Dietrich Klakow +1
In this work, we present AfriHuBERT, an extension of mHuBERT-147, a compact self-supervised learning (SSL) model pretrained on 147 languages. While mHuBERT-147 covered 16 African l…
Robust Pronoun Fidelity with English LLMs: Are they Reasoning, Repeating, or Just Biased?
Vagrant Gautam, Eileen Bingert, Dawei Zhu +2
Robust, faithful and harm-free pronoun use for individuals is an important goal for language model development as their use increases, but prior work tends to study only one or two…
AAdaM at SemEval-2024 Task 1: Augmentation and Adaptation for Multilingual Semantic Textual Relatedness
Miaoran Zhang, Mingyang Wang, Jesujoba O. Alabi +1
This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages. The shared task aims at measuring the semantic t…
What explains the success of cross-modal fine-tuning with ORCA?
Paloma García-de-Herreros, Vagrant Gautam, Philipp Slusallek +2
ORCA (Shen et al., 2023) is a recent technique for cross-modal fine-tuning, i.e., applying pre-trained transformer models to modalities beyond their training data. The technique co…
EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation
Atnafu Lambebo Tonja, Israel Abebe Azime, Tadesse Destaw Belay +10
Large language models (LLMs) have gained popularity recently due to their outstanding performance in various downstream Natural Language Processing (NLP) tasks. However, low-resour…
The Hidden Space of Transformer Language Adapters
Jesujoba O. Alabi, Marius Mosbach, Matan Eyal +2
We analyze the operation of transformer language adapters, which are small modules trained on top of a frozen language model to adapt its predictions to new target languages. We sh…
The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis
Miaoran Zhang, Vagrant Gautam, Mingyang Wang +4
In-context learning is a popular inference strategy where large language models solve a task using only a few labeled demonstrations without needing any parameter updates. Although…
Understanding and Mitigating Classification Errors Through Interpretable Token Patterns
Michael A. Hedderich, Jonas Fischer, Dietrich Klakow +1
State-of-the-art NLP methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives insight int…
Large GPT-like Models are Bad Babies: A Closer Look at the Relationship between Linguistic Competence and Psycholinguistic Measures
Julius Steuer, Marius Mosbach, Dietrich Klakow
Research on the cognitive plausibility of language models (LMs) has so far mostly concentrated on modelling psycholinguistic response variables such as reading times, gaze duration…
A Lightweight Method to Generate Unanswerable Questions in English
Vagrant Gautam, Miaoran Zhang, Dietrich Klakow
If a question cannot be answered with the available information, robust systems for question answering (QA) should know _not_ to answer. One way to build QA models that do this is…
Information-Theoretic Characterization of Vowel Harmony: A Cross-Linguistic Study on Word Lists
Julius Steuer, Badr Abdullah, Johann-Mattis List +1
We present a cross-linguistic study that aims to quantify vowel harmony using data-driven computational modeling. Concretely, we define an information-theoretic measure of harmonic…
On the N-gram Approximation of Pre-trained Language Models
Aravind Krishnan, Jesujoba Alabi, Dietrich Klakow
Large pre-trained language models (PLMs) have shown remarkable performance across various natural language understanding (NLU) tasks, particularly in low-resource settings. Neverth…
An Information-Theoretic Analysis of Self-supervised Discrete Representations of Speech
Badr M. Abdullah, Mohammed Maqsood Shaik, Bernd Möbius +1
Self-supervised representation learning for speech often involves a quantization step that transforms the acoustic input into discrete units. However, it remains unclear how to cha…
Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation
Marius Mosbach, Tiago Pimentel, Shauli Ravfogel +2
Few-shot fine-tuning and in-context learning are two alternative strategies for task adaptation of pre-trained language models. Recently, in-context learning has gained popularity…
MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages
Cheikh M. Bamba Dione, David Adelani, Peter Nabende +41
In this paper, we present MasakhaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these…
Weaker Than You Think: A Critical Look at Weakly Supervised Learning
Dawei Zhu, Xiaoyu Shen, Marius Mosbach +2
Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. Instead of requesting high-quality yet costly human annotations, it…
KÚ <MASK>: Integrating Yorùbá cultural greetings into machine translation
Idris Akinade, Jesujoba Alabi, David Adelani +2
This paper investigates the performance of massively multilingual neural machine translation (NMT) systems in translating Yorùbá greetings ( kú [MASK]), which are a bi…
Analyzing the Representational Geometry of Acoustic Word Embeddings
Badr M. Abdullah, Dietrich Klakow
Acoustic word embeddings (AWEs) are vector representations such that different acoustic exemplars of the same word are projected nearby in the embedding space. In addition to their…
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder +42
African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability o…
A Data-Driven Investigation of Noise-Adaptive Utterance Generation with Linguistic Modification
Anupama Chingacham, Vera Demberg, Dietrich Klakow
In noisy environments, speech can be hard to understand for humans. Spoken dialog systems can help to enhance the intelligibility of their output, either by modifying the speech sy…