We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs. Relative to traditional word representation models that have independent vectors for each word type, our model requires only a single vector per character type and a fixed set of parameters for the compositional model. Despite the compactness of this model and, more importantly, the arbitrary nature of the form-function relationship in language, our "composed" word representations yield state-of-the-art results in language modeling and part-of-speech tagging. Benefits over traditional baselines are particularly pronounced in morphologically rich languages (e.g., Turkish).
In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Extensive experimental results demonstrate that our transfer learning approach can outperform several strong baselines and achieve the state-of-the-art performance on two benchmark datasets.
In this paper, we build a corpus of tweets from Twitter annotated with keywords using crowdsourcing methods. We identify key differences between this domain and the work performed on other domains, such as news, which makes existing approaches for automatic keyword extraction not generalize well on Twitter datasets. These datasets include the small amount of content in each tweet, the frequent usage of lexical variants and the high variance of the cardinality of keywords present in each tweet. We propose methods for addressing these issues, which leads to solid improvements on this dataset for this task.
High-quality parallel data is crucial for a range of multilingual applications, from tuning and evaluating machine translation systems to cross-lingual annotation projection. Unfortunately, automatically obtained parallel data (which is available in relative abundance) tends to be quite noisy. To obtain high-quality parallel data, we introduce a crowdsourcing paradigm in which workers with only basic bilingual proficiency identify translations from an automatically extracted corpus of parallel microblog messages. For less than $350, we obtained over 5000 parallel segments in five language pairs. Evaluated against expert annotations, the quality of the crowdsourced corpus is significantly better than existing automatic methods: it obtains an performance comparable to expert annotations when used in MERT tuning of a microblog MT system; and training a parallel sentence classifier with it leads also to improved results. The crowdsourced corpora will be made available in
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