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20192024
most citedLSTM Language Models for LVCSR in First-Pass Decoding and Lattice-Rescoring

20 citations · 37 across the 15 of their papers we have counts for

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6 papers · 1 filter

cs.CL20231 cited

CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability

Minxuan Lv, Chengwei Dai, Kun Li +2

Neural network models are vulnerable to adversarial examples, and adversarial transferability further increases the risk of adversarial attacks. Current methods based on transferab…

cs.CL2023

Investigating the Effect of Language Models in Sequence Discriminative Training for Neural Transducers

Zijian Yang, Wei Zhou, Ralf Schlüter +1

In this work, we investigate the effect of language models (LMs) with different context lengths and label units (phoneme vs. word) used in sequence discriminative training for phon…

cs.CL20234 cited

Dialogue Shaping: Empowering Agents through NPC Interaction

Wei Zhou, Xiangyu Peng, Mark Riedl

One major challenge in reinforcement learning (RL) is the large amount of steps for the RL agent needs to converge in the training process and learn the optimal policy, especially…

cs.CL2023

UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis

Dou Hu, Lingwei Wei, Yaxin Liu +2

This paper describes our system designed for SemEval-2023 Task 12: Sentiment analysis for African languages. The challenge faced by this task is the scarcity of labeled data and li…

cs.CL2023

Stylized Data-to-Text Generation: A Case Study in the E-Commerce Domain

Liqiang Jing, Xuemeng Song, Xuming Lin +3

Existing data-to-text generation efforts mainly focus on generating a coherent text from non-linguistic input data, such as tables and attribute-value pairs, but overlook that diff…

cs.CL2023

Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation

Haolan Zhan, Xuming Lin, Shaobo Cui +3

Existing neural methods have shown great potentials towards generating informative text from structured tabular data as well as maintaining high content fidelity. However, few of t…