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20222026
most citedEfficient Monotonic Multihead Attention

5 citations · 15 across the 18 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CL2024

CA*: Addressing Evaluation Pitfalls in Computation-Aware Latency for Simultaneous Speech Translation

Xi Xu, Wenda Xu, Siqi Ouyang +1

Simultaneous speech translation (SimulST) systems must balance translation quality with response time, making latency measurement crucial for evaluating their real-world performanc…

cs.CL2024

Translation Canvas: An Explainable Interface to Pinpoint and Analyze Translation Systems

Chinmay Dandekar, Wenda Xu, Xi Xu +2

With the rapid advancement of machine translation research, evaluation toolkits have become essential for benchmarking system progress. Tools like COMET and SacreBLEU offer single…

cs.CL2024

Anticipating Future with Large Language Model for Simultaneous Machine Translation

Siqi Ouyang, Oleksii Hrinchuk, Zhehuai Chen +4

Simultaneous machine translation (SMT) takes streaming input utterances and incrementally produces target text. Existing SMT methods mainly use the partial utterance that has alrea…

cs.CL2024★ 1 cited

FASST: Fast LLM-based Simultaneous Speech Translation

Siqi Ouyang, Xi Xu, Chinmay Dandekar +1

Simultaneous speech translation (SST) takes streaming speech input and generates text translation on the fly. Existing methods either have high latency due to recomputation of inpu…

cs.CL2024

CMU's IWSLT 2024 Simultaneous Speech Translation System

Xi Xu, Siqi Ouyang, Brian Yan +5

This paper describes CMU's submission to the IWSLT 2024 Simultaneous Speech Translation (SST) task for translating English speech to German text in a streaming manner. Our end-to-e…

q-bio.BM2024★ 1 cited

Structure-Based Drug Design via 3D Molecular Generative Pre-training and Sampling

Yuwei Yang, Siqi Ouyang, Xueyu Hu +3

Structure-based drug design aims at generating high affinity ligands with prior knowledge of 3D target structures. Existing methods either use conditional generative model to learn…