148 research outputs found
MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching
Text matching is the core problem in many natural language processing (NLP)
tasks, such as information retrieval, question answering, and conversation.
Recently, deep leaning technology has been widely adopted for text matching,
making neural text matching a new and active research domain. With a large
number of neural matching models emerging rapidly, it becomes more and more
difficult for researchers, especially those newcomers, to learn and understand
these new models. Moreover, it is usually difficult to try these models due to
the tedious data pre-processing, complicated parameter configuration, and
massive optimization tricks, not to mention the unavailability of public codes
sometimes. Finally, for researchers who want to develop new models, it is also
not an easy task to implement a neural text matching model from scratch, and to
compare with a bunch of existing models. In this paper, therefore, we present a
novel system, namely MatchZoo, to facilitate the learning, practicing and
designing of neural text matching models. The system consists of a powerful
matching library and a user-friendly and interactive studio, which can help
researchers: 1) to learn state-of-the-art neural text matching models
systematically, 2) to train, test and apply these models with simple
configurable steps; and 3) to develop their own models with rich APIs and
assistance
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
Matching natural language sentences is central for many applications such as
information retrieval and question answering. Existing deep models rely on a
single sentence representation or multiple granularity representations for
matching. However, such methods cannot well capture the contextualized local
information in the matching process. To tackle this problem, we present a new
deep architecture to match two sentences with multiple positional sentence
representations. Specifically, each positional sentence representation is a
sentence representation at this position, generated by a bidirectional long
short term memory (Bi-LSTM). The matching score is finally produced by
aggregating interactions between these different positional sentence
representations, through -Max pooling and a multi-layer perceptron. Our
model has several advantages: (1) By using Bi-LSTM, rich context of the whole
sentence is leveraged to capture the contextualized local information in each
positional sentence representation; (2) By matching with multiple positional
sentence representations, it is flexible to aggregate different important
contextualized local information in a sentence to support the matching; (3)
Experiments on different tasks such as question answering and sentence
completion demonstrate the superiority of our model.Comment: Accepted by AAAI-201
- …