A description of a system for identifying Verbal Multi-Word Expressions (VMWEs) in running text is presented. The system mainly exploits universal syntactic dependency features through a Conditional Random Fields (CRF) sequence model. The system competed in the Closed Track at the PARSEME VMWE Shared Task 2017, ranking 2nd place in most languages on full VMWE-based evaluation and 1st in three languages on token-based evaluation. In addition, this paper presents an option to re-rank the 10 best CRF-predicted sequences via semantic vectors, boosting its scores above other systems in the competition. We also show that all systems in the competition would struggle to beat a simple lookup baseline system and argue for a more purposespecific evaluation scheme.

We introduce the Machine Translation (MT) evaluation survey that contains both manual and automatic evaluation methods. The traditional human evaluation criteria mainly include the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness. The advanced human assessments include task-oriented measures, post-editing, segment ranking, and extended criteriea, etc. We classify the automatic evaluation methods into two categories, including lexical similarity scenario and linguistic features application. The lexical similarity methods contain edit distance, precision, recall, F-measure, and word order. The linguistic features can be divided into syntactic features and semantic features respectively. The syntactic features include part of speech tag, phrase types and sentence structures, and the semantic features include named entity, synonyms, textual entailment, paraphrase, semantic roles, and language models. The deep learning models for evaluation are very newly proposed. Subsequently, we also introduce the evaluation methods for MT evaluation including different correlation scores, and the recent quality estimation (QE) tasks for MT. This paper differs from the existing works (Dorr et al., 2009; EuroMatrix, 2007) from several aspects, by introducing some recent development of MT evaluation measures, the different classifications from manual to automatic evaluation measures, the introduction of recent QE tasks of MT, and the concise construction of the content.We hope this work will be helpful for MT researchers to easily pick up some metrics that are best suitable for their specific MT model development, and help MT evaluation researchers to get a general clue of how MT evaluation research developed. Furthermore, hopefully, this work can also shine some light on other evaluation tasks, except for translation, of NLP fields. 1

Information and reference services are one major component of library services. This article attempts to describe the paradigm of information and reference services in the digital library. Based on the fact that automatic digital library technologies are solving more and more information needs and changing the mode of user service, the authors suggest a three-levelled system that supports users' information needs. The role of reference librarians at each level is discussed. Finally, digital reference service, a new means of delivering services, is briefly reviewed. The authors emphasize that a systematic process to support users' information needs in the digital library is required.

Purpose Student participation has been an important issue for information literacy (IL) teachings. The purpose of this paper is to promote active student participation in IL courses with Rain Classroom, an intelligent teaching tool. Design/methodology/approach Using mixed method research, the paper presents a practical case study of the author’s experiences with Rain Classroom to improve teaching and learning of IL. Findings The study shows that Rain Classroom helps implement problem-based learning, promote student participation in class interaction and optimize learning experience, which facilitates a shift of the IL course from passive to active learning. Research limitations/implications It is known that university public courses have large class sizes (more than 50 students per class), and, therefore, class interaction is difficult to organize. So this is a big issue for the researchers to study. Practical implications The proposed Rain Classroom is a free teaching tool and can be used in other academic libraries to enhance active student participation in IL lessons. Social implications The paper includes implications for improving interaction in large-size conference or trainings using Rain Classroom. Originality/value The existing literature has not traced the reports on using the Rain Classroom to enhance student participation in IL courses in academic libraries. This paper intends to fill this gap and share practical methods and experiences, deepening the application research of Rain Classroom.