In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Wednesdays, and each time a keynote speaker will share understanding of papers published in recent years with you.
This week’s seminar is organized as follows: 1. The seminar time is 1.pm, Wed., at Zhongguancun Technology Park ,Building 5, 1306. 2. The lecturer is Baohua Zhang, the paper’s title is Curriculum Learning for Natural Answer Generation. 3. The seminar will be hosted by Zhaoyang Wang. 4. Attachment is the paper of this seminar, please download in advance.
Anyone interested in this topic is welcomed to join us. the following is the abstract for this week’s paper.
Curriculum Learning for Natural Answer Generation
Cao Liu, Shizhu He, Kang
Liu, Jun Zhao
By reason of being able to obtain natural
language responses, natural answers are more favored in real-world Question
Answering (QA) systems. Generative models learn to automatically generate
natural answers from large-scale question answer pairs (QA-pairs). However,
they are suffering from the uncontrollable and uneven quality of QA-pairs
crawled from the Internet. To address this problem, we propose a curriculum
learning based framework for natural answer generation (CL-NAG), which is able
to take full advantage of the valuable learning data from a noisy and
uneven-quality corpora. Specifically, we employ two practical measures to
automatically measure the quality (complexity) of QA-pairs. Based on the
measurements, CLNAG firstly utilizes simple and low-quality QApairs to learn a
basic model, and then gradually learns to produce better answers with richer
contents and more complete syntaxes based on more complex and higher-quality
QA-pairs. In this way, all valuable information in the noisy and unevenquality
corpora could be fully exploited. Experiments demonstrate that CL-NAG
outperforms the state-of-the-art, which increases 6.8% and 8.7% in the accuracy
for simple and complex questions, respectively.