Part 1. Definition
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Part 2. Example
We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.
Question: Are results reported only for English data?
Answer: No
Explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.
Part 3. Exercise
Results in Table TABREF13 show ten probes. Again, we see the performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks. As we would expect, on the probe for predicting chunk tags, mSynC achieves 96.9 $F_1$ vs. 92.2 $F_1$ for ELMo-transformer, indicating that mSynC is indeed encoding shallow syntax. Overall, the results further confirm that explicit shallow syntax does not offer any benefits over ELMo-transformer.
Question: What are improvements for these two approaches relative to ELMo-only baselines?
Answer:
only modest gains on three of the four downstream tasks