Jia Liu


Spelling Correction using Phonetics in E-commerce Search
Fan Yang | Alireza Bagheri Garakani | Yifei Teng | Yan Gao | Jia Liu | Jingyuan Deng | Yi Sun
Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries. However, resolving phonetic errors is a critical but much overlooked area. The query with phonetic spelling errors tends to appear correct based on pronunciation but is nonetheless inaccurate in spelling (e.g., “bluetooth sound system” vs. “blutut sant sistam”) with numerous noisy forms and sparse occurrences. In this work, we propose a generalized spelling correction system integrating phonetics to address phonetic errors in E-commerce search without additional latency cost. Using India (IN) E-commerce market for illustration, the experiment shows that our proposed phonetic solution significantly improves the F1 score by 9%+ and recall of phonetic errors by 8%+. This phonetic spelling correction system has been deployed to production, currently serving hundreds of millions of customers.

Improve Interpretability of Neural Networks via Sparse Contrastive Coding
Junhong Liu | Yijie Lin | Liang Jiang | Jia Liu | Zujie Wen | Xi Peng
Findings of the Association for Computational Linguistics: EMNLP 2022

Although explainable artificial intelligence (XAI) has achieved remarkable developments in recent years, there are few efforts have been devoted to the following problems, namely, i) how to develop an explainable method that could explain the black-box in a model-agnostic way? and ii) how to improve the performance and interpretability of the black-box using such explanations instead of pre-collected important attributions? To explore the potential solution, we propose a model-agnostic explanation method termed as Sparse Contrastive Coding (SCC) and verify its effectiveness in text classification and natural language inference. In brief, SCC explains the feature attributions which characterize the importance of words based on the hidden states of each layer of the model. With such word-level explainability, SCC adaptively divides the input sentences into foregrounds and backgrounds in terms of task relevance. Through maximizing the similarity between the foregrounds and input sentences while minimizing the similarity between the backgrounds and input sentences, SSC employs a supervised contrastive learning loss to boost the interpretability and performance of the model. Extensive experiments show the superiority of our method over five state-of-the-art methods in terms of interpretability and classification measurements. The code is available at https://pengxi.me.


Manifolds Based Emotion Recognition in Speech
Mingyu You | Chun Chen | Jiajun Bu | Jia Liu | Jianhua Tao
International Journal of Computational Linguistics & Chinese Language Processing, Volume 12, Number 1, March 2007: Special Issue on Affective Speech Processing