342 citations · 678 across the 23 of their papers we have counts for
29 papers
Unraveling and Mitigating Safety Alignment Degradation of Vision-Language Models
Qin Liu, Chao Shang, Ling Liu +7
The safety alignment ability of Vision-Language Models (VLMs) is prone to be degraded by the integration of the vision module compared to its LLM backbone. We investigate this phen…
Machine Translation Evaluation Meets Community Question Answering
Francisco Guzmán, Lluís Màrquez, Preslav Nakov
We explore the applicability of machine translation evaluation (MTE) methods to a very different problem: answer ranking in community Question Answering. In particular, we adopt a…
Pairwise Neural Machine Translation Evaluation
Francisco Guzman, Shafiq Joty, Lluis Marquez +1
We present a novel framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypot…
SemEval-2016 Task 3: Community Question Answering
Preslav Nakov, Lluís Màrquez, Alessandro Moschitti +5
This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic. For English, we had three subtasks: Question--Comment Similar…
SemEval-2017 Task 3: Community Question Answering
Preslav Nakov, Doris Hoogeveen, Lluís Màrquez +4
We describe SemEval-2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016:(A) Question-Comment Similarity,(B) Question-Question Simil…
DiscoTK: Using Discourse Structure for Machine Translation Evaluation
Shafiq Joty, Francisco Guzman, Lluis Marquez +1
We present novel automatic metrics for machine translation evaluation that use discourse structure and convolution kernels to compare the discourse tree of an automatic translation…