Sai Zhang


A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots
Sai Zhang | Yuwei Hu | Yuchuan Wu | Jiaman Wu | Yongbin Li | Jian Sun | Caixia Yuan | Xiaojie Wang
Findings of the Association for Computational Linguistics: ACL 2022

A slot value might be provided segment by segment over multiple-turn interactions in a dialog, especially for some important information such as phone numbers and names. It is a common phenomenon in daily life, but little attention has been paid to it in previous work. To fill the gap, this paper defines a new task named Sub-Slot based Task-Oriented Dialog (SSTOD) and builds a Chinese dialog dataset SSD for boosting research on SSTOD. The dataset includes a total of 40K dialogs and 500K utterances from four different domains: Chinese names, phone numbers, ID numbers and license plate numbers. The data is well annotated with sub-slot values, slot values, dialog states and actions. We find some new linguistic phenomena and interactive manners in SSTOD which raise critical challenges of building dialog agents for the task. We test three state-of-the-art dialog models on SSTOD and find they cannot handle the task well on any of the four domains. We also investigate an improved model by involving slot knowledge in a plug-in manner. More work should be done to meet the new challenges raised from SSTOD which widely exists in real-life applications. The dataset and code are publicly available via


Ferryman at SemEval-2020 Task 3: Bert with TFIDF-Weighting for Predicting the Effect of Context in Word Similarity
Weilong Chen | Xin Yuan | Sai Zhang | Jiehui Wu | Yanru Zhang | Yan Wang
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Word similarity is widely used in machine learning applications like searching engine and recommendation. Measuring the changing meaning of the same word between two different sentences is not only a way to handle complex features in word usage (such as sentence syntax and semantics), but also an important method for different word polysemy modeling. In this paper, we present the methodology proposed by team Ferryman. Our system is based on the Bidirectional Encoder Representations from Transformers (BERT) model combined with term frequency-inverse document frequency (TF-IDF), applying the method on the provided datasets called CoSimLex, which covers four different languages including English, Croatian, Slovene, and Finnish. Our team Ferryman wins the the first position for English task and the second position for Finnish in the subtask 1.