Enhance word representation for out-of-vocabulary on Ubuntu dialogue corpus
This work addresses a domain-specific challenge in dialogue systems by improving word representation for out-of-vocabulary terms, though it is incremental as it builds upon existing methods like ESIM.
The paper tackled the problem of out-of-vocabulary words in the Ubuntu dialogue corpus by combining general pre-trained word embeddings with task-specific ones, achieving state-of-the-art results on next utterance selection tasks with significant performance improvements over the original ESIM method.
Ubuntu dialogue corpus is the largest public available dialogue corpus to make it feasible to build end-to-end deep neural network models directly from the conversation data. One challenge of Ubuntu dialogue corpus is the large number of out-of-vocabulary words. In this paper we proposed a method which combines the general pre-trained word embedding vectors with those generated on the task-specific training set to address this issue. We integrated character embedding into Chen et al's Enhanced LSTM method (ESIM) and used it to evaluate the effectiveness of our proposed method. For the task of next utterance selection, the proposed method has demonstrated a significant performance improvement against original ESIM and the new model has achieved state-of-the-art results on both Ubuntu dialogue corpus and Douban conversation corpus. In addition, we investigated the performance impact of end-of-utterance and end-of-turn token tags.