  This paper presents an end-to-end response selection model for Track 1 of the
7th Dialogue System Technology Challenges (DSTC7). This task focuses on
selecting the correct next utterance from a set of candidates given a partial
conversation. We propose an end-to-end neural network based on enhanced
sequential inference model (ESIM) for this task. Our proposed model differs
from the original ESIM model in the following four aspects. First, a new word
representation method which combines the general pre-trained word embeddings
with those estimated on the task-specific training set is adopted in order to
address the challenge of out-of-vocabulary (OOV) words. Second, an attentive
hierarchical recurrent encoder (AHRE) is designed which is capable to encode
sentences hierarchically and generate more descriptive representations by
aggregation. Third, a new pooling method which combines multi-dimensional
pooling and last-state pooling is used instead of the simple combination of max
pooling and average pooling in the original ESIM. Last, a modification layer is
added before the softmax layer to emphasize the importance of the last
utterance in the context for response selection. In the released evaluation
results of DSTC7, our proposed method ranked second on the Ubuntu dataset and
third on the Advising dataset in subtask 1 of Track 1.
