38 citations · 100 across the 9 of their papers we have counts for
9 papers
CulturALL: Benchmarking Multilingual and Multicultural Competence of LLMs on Grounded Tasks
Peiqin Lin, Chenyang Lyu, Wenjiang Luo +22
Large language models (LLMs) are now deployed worldwide, inspiring a surge of benchmarks that measure their multilingual and multicultural abilities. However, these benchmarks prio…
TransBench: Benchmarking Machine Translation for Industrial-Scale Applications
Haijun Li, Tianqi Shi, Zifu Shang +13
Machine translation (MT) has become indispensable for cross-border communication in globalized industries like e-commerce, finance, and legal services, with recent advancements in…
Parallel Data Helps Neural Entity Coreference Resolution
Gongbo Tang, Christian Hardmeier
Coreference resolution is the task of finding expressions that refer to the same entity in a text. Coreference models are generally trained on monolingual annotated data but annota…
Revisiting Negation in Neural Machine Translation
Gongbo Tang, Philipp Rönchen, Rico Sennrich +1
In this paper, we evaluate the translation of negation both automatically and manually, in English--German (EN--DE) and English--Chinese (EN--ZH). We show that the ability of neura…
Encoders Help You Disambiguate Word Senses in Neural Machine Translation
Gongbo Tang, Rico Sennrich, Joakim Nivre
Neural machine translation (NMT) has achieved new state-of-the-art performance in translating ambiguous words. However, it is still unclear which component dominates the process of…
An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation
Gongbo Tang, Rico Sennrich, Joakim Nivre
Recent work has shown that the encoder-decoder attention mechanisms in neural machine translation (NMT) are different from the word alignment in statistical machine translation. In…
Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures
Gongbo Tang, Mathias Müller, Annette Rios +1
Recently, non-recurrent architectures (convolutional, self-attentional) have outperformed RNNs in neural machine translation. CNNs and self-attentional networks can connect distant…
An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization
Gongbo Tang, Fabienne Cap, Eva Pettersson +1
In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish. The NMT mod…
The Helsinki Neural Machine Translation System
Robert Östling, Yves Scherrer, Jörg Tiedemann +2
We introduce the Helsinki Neural Machine Translation system (HNMT) and how it is applied in the news translation task at WMT 2017, where it ranked first in both the human and autom…