In this paper, we introduce an approach to combining word embeddings and machine translation for multilingual semantic word similarity, the task2 of SemEval-2017. Thanks to the unsupervised translit-eration model, our cross-lingual word em-beddings encounter decreased sums of OOVs. Our results are produced using only monolingual Wikipedia corpora and a limited amount of sentence-aligned data. Although relatively little resources are utilized , our system ranked 3rd in the mono-lingual subtask and can be the 6th in the cross-lingual subtask.

The "Affective Text" task focuses on the classification of emotions and valence (positive/negative polarity) in news headlines, and is meant as an exploration of the connection between emotions and lexical semantics. In this paper, we describe the data set used in the evaluation and the results obtained by the participating systems.

To model semantic similarity for multilingual and cross-lingual sentence pairs, we first translate foreign languages into En-glish, and then build an efficient mono-lingual English system with multiple NLP features. Our system is further supported by deep learning models and our best run achieves the mean Pearson correlation 73.16% in primary track.

This paper describes experiments concerned with the automatic analysis of emotions in text. We describe the construction of a large data set annotated for six basic emotions: anger, disgust, fear, joy, sadness and surprise, and we propose and evaluate several knowledge-based and corpusbased methods for the automatic identification of these emotions in text.

Emotion recognition in conversations (ERC) is a challenging task that has recently gained popularity due to its potential applications. Until now, however, there has been no largescale multimodal multi-party emotional conversational database containing more than two speakers per dialogue. To address this gap, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual, and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at