148 citations · 255 across the 14 of their papers we have counts for
16 papers
Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models
Miguel Romero, Shuoyang Ding, Corey D. Barret +2
Dense embeddings are fundamental to modern machine learning systems, powering Retrieval-Augmented Generation (RAG), information retrieval, and representation learning. While instru…
RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation
Gabriele Sarti, Phu Mon Htut, Xing Niu +4
Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of translation o…
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation
Anna Currey, Maria Nădejde, Raghavendra Pappagari +5
As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased. In particular, gender accurac…
A baseline revisited: Pushing the limits of multi-segment models for context-aware translation
Suvodeep Majumder, Stanislas Lauly, Maria Nadejde +2
This paper addresses the task of contextual translation using multi-segment models. Specifically we show that increasing model capacity further pushes the limits of this approach a…
CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality
Maria Nădejde, Anna Currey, Benjamin Hsu +3
The machine translation (MT) task is typically formulated as that of returning a single translation for an input segment. However, in many cases, multiple different translations ar…
Faithful Target Attribute Prediction in Neural Machine Translation
Xing Niu, Georgiana Dinu, Prashant Mathur +1
The training data used in NMT is rarely controlled with respect to specific attributes, such as word casing or gender, which can cause errors in translations. We argue that predict…
Improving Gender Translation Accuracy with Filtered Self-Training
Prafulla Kumar Choubey, Anna Currey, Prashant Mathur +1
Targeted evaluations have found that machine translation systems often output incorrect gender, even when the gender is clear from context. Furthermore, these incorrectly gendered…
Evaluating Robustness to Input Perturbations for Neural Machine Translation
Xing Niu, Prashant Mathur, Georgiana Dinu +1
Neural Machine Translation (NMT) models are sensitive to small perturbations in the input. Robustness to such perturbations is typically measured using translation quality metrics…
Joint translation and unit conversion for end-to-end localization
Georgiana Dinu, Prashant Mathur, Marcello Federico +2
A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions. In this paper…
Training Neural Machine Translation To Apply Terminology Constraints
Georgiana Dinu, Prashant Mathur, Marcello Federico +1
This paper proposes a novel method to inject custom terminology into neural machine translation at run time. Previous works have mainly proposed modifications to the decoding algor…
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection
Jian Ni, Georgiana Dinu, Radu Florian
The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. H…
Reinforcement Learning for Transition-Based Mention Detection
Georgiana Dinu, Wael Hamza, Radu Florian
This paper describes an application of reinforcement learning to the mention detection task. We define a novel action-based formulation for the mention detection task, in which a m…
Toward Mention Detection Robustness with Recurrent Neural Networks
Thien Huu Nguyen, Avirup Sil, Georgiana Dinu +1
One of the key challenges in natural language processing (NLP) is to yield good performance across application domains and languages. In this work, we investigate the robustness of…
From Visual Attributes to Adjectives through Decompositional Distributional Semantics
Angeliki Lazaridou, Georgiana Dinu, Adam Liska +1
As automated image analysis progresses, there is increasing interest in richer linguistic annotation of pictures, with attributes of objects (e.g., furry, brown...) attracting most…
Improving zero-shot learning by mitigating the hubness problem
Georgiana Dinu, Angeliki Lazaridou, Marco Baroni
The zero-shot paradigm exploits vector-based word representations extracted from text corpora with unsupervised methods to learn general mapping functions from other feature spaces…
Multi-Step Regression Learning for Compositional Distributional Semantics
Edward Grefenstette, Georgiana Dinu, Yao-Zhong Zhang +2
We present a model for compositional distributional semantics related to the framework of Coecke et al. (2010), and emulating formal semantics by representing functions as tensors…