TASK DEFINITION: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
PROBLEM: Thus we propose two kinds of relation-specific meta information: relation meta and gradient meta corresponding to afore mentioned two perspectives respectively. In our proposed framework MetaR, relation meta is the high-order representation of a relation connecting head and tail entities. Gradient meta is the loss gradient of relation meta which will be used to make a rapid update before transferring relation meta to incomplete triples during prediction.

SOLUTION: What meta-information is being transferred?

PROBLEM: Furthermore, we employ two different parsers for comparison: 1) the PCFGLA-based parser, viz. Berkeley parser BIBREF5 , and 2) a minimal span-based neural parser BIBREF6 .

SOLUTION: What is the baseline model for the agreement-based mode?

PROBLEM: We use the publicly available dataset KVRET BIBREF5 in our experiments. There are 2,425 dialogues for training, 302 for validation and 302 for testing, as shown in the upper half of Table TABREF12.

SOLUTION:
What is the size of the dataset?