Jinsong Su

Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks

2018 · arXiv (Cornell University)

In this paper, we propose an additionsubtraction twin-gated recurrent network (ATR) to simplify neural machine translation. The recurrent units of ATR are heavily simplified to have the smallest number of weight matrices among units of …

KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation

2021 · arXiv (Cornell University)

Generative commonsense reasoning requires machines to generate sentences describing an everyday scenario given several concepts, which has attracted much attention recently. However, existing models cannot perform as well as humans, since sentences they produce are …

A Learning Rate Path Switching Training Paradigm for Version Updates of Large Language Models

2024

Due to the continuous emergence of new data, version updates have become an indispensable requirement for Large Language Models (LLMs).The training paradigms for version updates of LLMs include pre-training from scratch (PTFS) and continual pre-training …

Advancing SMoE for Continuous Domain Adaptation of MLLMs: Adaptive Router and Domain-Specific Loss

2025

Recent studies have explored Continual Instruction Tuning (CIT) in Multimodal Large Language Models (MLLMs), with a primary focus on Task-incremental CIT, where MLLMs are required to continuously acquire new tasks.However, the more practical and challenging …

PLaST: Towards Paralinguistic-aware Speech Translation

2026 · Proceedings of the AAAI Conference on Artificial Intelligence

Speech translation (ST) aims to translate speech from a source language into text in the target language. Naturally, speech signals contain paralinguistic cues beyond linguistic content, which could influence or even alter the interpretation of …

Semantic Neural Machine Translation Using AMR

2019 · Transactions of the Association for Computational Linguistics

Abstract It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation …

Variational Neural Machine Translation

2016

Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence. In this paper, we propose a variational model to …

Iterative Dual Domain Adaptation for Neural Machine Translation

2019

Jiali Zeng, Yang Liu, Jinsong Su, Yubing Ge, Yaojie Lu, Yongjing Yin, Jiebo Luo. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language …