Bo Wang

Bio: Bo Wang is an academic researcher from Tianjin University. The author has contributed to research in topics: Machine translation & Computer science. The author has an hindex of 6, co-authored 29 publications receiving 116 citations. Previous affiliations of Bo Wang include Harbin Institute of Technology.

Diagnostic Evaluation of Machine Translation Systems Using Automatically Constructed Linguistic Check-Points

Abstract: We present a diagnostic evaluation platform which provides multi-factored evaluation based on automatically constructed check-points. A check-point is a linguistically motivated unit (e.g. an ambiguous word, a noun phrase, a verb~obj collocation, a prepositional phrase etc.), which are pre-defined in a linguistic taxonomy. We present a method that automatically extracts check-points from parallel sentences. By means of checkpoints, our method can monitor a MT system in translating important linguistic phenomena to provide diagnostic evaluation. The effectiveness of our approach for diagnostic evaluation is verified through experiments on various types of MT systems.

Social Recommendation with Implicit Social Influence

Abstract: Social influence is essential to social recommendation. Current influence-based social recommendation focuses on the explicit influence on observed social links. However, in real cases, implicit social influence can also impact users' preference in an unobserved way. In this work, we concern two kinds of implicit influence: Local Implicit Influence of persons on unobserved interpersonal relations, and Global Implicit Influence of items broadcasted to users. We improve the state-of-the-art GNN-based social recommendation methods by modeling two kinds of implicit influences separately. Local implicit influence is involved by predicting unobserved social relationships. Global implicit influence is involved by defining global popularity of each item and personalize the impact of the popularity on each user. In a GCN network, explicit and implicit influence are integrated to learn the social embedding of users and items in social recommendation. Experimental results on Yelp initially demonstrate the effectiveness of proposed model.

Automatic diagnosis and evaluation method for machine translation

Abstract: The invention relates to an automatic diagnosis and evaluation method for machine translation, belonging to a machine translation evaluation technology and solving the problems that an evaluation method of the prior translation system can only test the processing capability of the translation system to a special monolingual phenomenon and can not obtain the defects of the translation system. The method comprises the following steps: firstly, matching words of a reference translation text and a systemic translation text and finding possible source language words for each object language word by utilizing translation knowledge; then, identifying errors and adopting the relation between source language and object language to judge a bilingual type of each error; and further utilizing the relation between the bilingual characteristics and the translation knowledge to judge the reasons of the errors. The automatic diagnosis and evaluation method for machine translation represents the bilingual errors by relative words in source language sentences, the reference translation text and the systemic translation text, induces the linguistics characteristics of the words in the diagnosis process and can more directly help developers to find and solve the inherent defects of the translation system.

Towards automatic error analysis of machine translation output

Abstract: Evaluation and error analysis of machine translation output are important but difficult tasks. In this article, we propose a framework for automatic error analysis and classification based on the identification of actual erroneous words using the algorithms for computation of Word Error Rate (WER) and Position-independent word Error Rate (PER), which is just a very first step towards development of automatic evaluation measures that provide more specific information of certain translation problems. The proposed approach enables the use of various types of linguistic knowledge in order to classify translation errors in many different ways. This work focuses on one possible set-up, namely, on five error categories: inflectional errors, errors due to wrong word order, missing words, extra words, and incorrect lexical choices. For each of the categories, we analyze the contribution of various POS classes. We compared the results of automatic error analysis with the results of human error analysis in order to investigate two possible applications: estimating the contribution of each error type in a given translation output in order to identify the main sources of errors for a given translation system, and comparing different translation outputs using the introduced error categories in order to obtain more information about advantages and disadvantages of different systems and possibilites for improvements, as well as about advantages and disadvantages of applied methods for improvements. We used Arabic-English Newswire and Broadcast News and Chinese-English Newswire outputs created in the framework of the GALE project, several Spanish and English European Parliament outputs generated during the TC-Star project, and three German-English outputs generated in the framework of the fourth Machine Translation Workshop. We show that our results correlate very well with the results of a human error analysis, and that all our metrics except the extra words reflect well the differences between different versions of the same translation system as well as the differences between different translation systems.

compare-mt: A Tool for Holistic Comparison of Language Generation Systems

Abstract: In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation. The main goal of the tool is to give the user a high-level and coherent view of the salient differences between systems that can then be used to guide further analysis or system improvement. It implements a number of tools to do so, such as analysis of accuracy of generation of particular types of words, bucketed histograms of sentence accuracies or counts based on salient characteristics, and extraction of characteristic n-grams for each system. It also has a number of advanced features such as use of linguistic labels, source side data, or comparison of log likelihoods for probabilistic models, and also aims to be easily extensible by users to new types of analysis. compare-mt is a pure-Python open source package, that has already proven useful to generate analyses that have been used in our published papers. Demo Video: https://youtu.be/NyJEQT7t2CA

Real-Time Video Emotion Recognition based on Reinforcement Learning and Domain Knowledge

Abstract: Multimodal emotion recognition in conversational videos (ERC) develops rapidly in recent years. To fully extract the relative context from video clips, most studies build their models on the entire dialogues which make them lack of real-time ERC ability. Different from related researches, a novel multimodal emotion recognition model for conversational videos based on reinforcement learning and domain knowledge (ERLDK) is proposed in this paper. In ERLDK, the reinforcement learning algorithm is introduced to conduct real-time ERC with the occurrence of conversations. The collection of history utterances is composed as an emotion-pair which represents the multimodal context of the following utterance to be recognized. Dueling deep-Q-network (DDQN) based on gated recurrent unit (GRU) layers is designed to learn the correct action from the alternative emotion categories. Domain knowledge is extracted from public dataset based on the former information of emotion-pairs. The extracted domain knowledge is used to revise the results from the RL module and is transformed into other dataset to examine the rationality. The experimental results on datasets show that ERLDK achieves the state-of-the-art results on weighted average and most of the specific emotion categories.