UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement
نویسندگان
چکیده
We describe an attention-based convolutional neural network for the English semantic textual similarity (STS) task in the SemEval2016 competition (Agirre et al., 2016). We develop an attention-based input interaction layer and incorporate it into our multiperspective convolutional neural network (He et al., 2015), using the PARAGRAM-PHRASE word embeddings (Wieting et al., 2016) trained on paraphrase pairs. Without using any sparse features, our final model outperforms the winning entry in STS2015 when evaluated on the STS2015 data.
منابع مشابه
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks
Modeling sentence similarity is complicated by the ambiguity and variability of linguistic expression. To cope with these challenges, we propose a model for comparing sentences that uses a multiplicity of perspectives. We first model each sentence using a convolutional neural network that extracts features at multiple levels of granularity and uses multiple types of pooling. We then compare our...
متن کاملHCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity
This paper describes our convolutional neural network (CNN) system for the Semantic Textual Similarity (STS) task. We calculated semantic similarity score between two sentences by comparing their semantic vectors. We generated a semantic vector by max pooling over every dimension of all word vectors in a sentence. There are two key design tricks used by our system. One is that we trained a CNN ...
متن کاملRiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks
In this paper, we present our systems for “SemEval-2017 Task-5 on Fine-Grained Sentiment Analysis on Financial Microblogs and News”. In our system, we combined hand-engineered lexical, sentiment, and metadata features with the representations learned from Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU) having Attention model applied on top. With this architec...
متن کاملNeobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model
This paper describes a neural-network model which performed competitively (top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS) task. Our system employs an attention-based recurrent neural network model that optimizes the sentence similarity. In this paper, we describe our participation in the multilingual STS task which measures similarity across English, Spanish, and Ara...
متن کاملUTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation
In this paper, we propose a deep neural network based natural language processing system for semantic textual similarity prediction. We leverage multi-layer bidirectional LSTM to learn sentence representation. After that, we construct matching features followed by Highway Multilayer Perceptron to make predictions. Experimental results demonstrate that this approach can’t get better results on s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2016