13 citations · 22 across the 7 of their papers we have counts for
7 papers
Toward Unified Controllable Text Generation via Regular Expression Instruction
Xin Zheng, Hongyu Lin, Xianpei Han +1
Controllable text generation is a fundamental aspect of natural language generation, with numerous methods proposed for different constraint types. However, these approaches often…
DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade
Zefan Cai, Xin Zheng, Tianyu Liu +7
In the constant updates of the product dialogue systems, we need to retrain the natural language understanding (NLU) model as new data from the real users would be merged into the…
DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog
Xin Zheng, Tianyu Liu, Haoran Meng +6
Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or…
DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection
Haoran Meng, Zheng Xin, Tianyu Liu +6
While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance. Instead of training a dedicated multi-intent detection model, we propose DialogUS…
Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View
Tianyu Liu, Xin Zheng, Baobao Chang +1
In open domain table-to-text generation, we notice that the unfaithful generation usually contains hallucinated content which can not be aligned to any input table record. We thus…
An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference
Tianyu Liu, Xin Zheng, Xiaoan Ding +2
The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust. In this paper, we focus…
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference
Tianyu Liu, Xin Zheng, Baobao Chang +1
Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothe…