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.

Ex Input:
We conduct experiments on our self-collected CAIS to evaluate the generalizability in different language. We apply two baseline models for comparison, one is the popular BiLSTMs + CRF architecture BIBREF36 for sequence labeling task, and the other one is the more powerful sententce-state LSTM BIBREF21. The results listed in Table TABREF50 demonstrate the generalizability and effectiveness of our CM-Net when handling various domains and different languages.
Question: What were the baselines models?

Ex Output:
BiLSTMs + CRF architecture BIBREF36 sententce-state LSTM BIBREF21

Ex Input:
In this paper, we first highlight the importance of TV and radio broadcast as a source of data for ASR, and the potential impact it can have. We then perform a statistical analysis of gender representation in a data set composed of four state-of-the-art corpora of French broadcast, widely used within the speech community. Finally we question the impact of such a representation on the systems developed on this data, through the perspective of an ASR system.
Question: What tasks did they use to evaluate performance for male and female speakers?

Ex Output:
ASR

Ex Input:
We focus on three main issues, including, how to incorporate affective information into chatbots, what are resources that available and can be used to build EAC, and how to evaluate EAC performance.
Question: What are the research questions posed in the paper regarding EAC studies?

Ex Output:
how to incorporate affective information into chatbots, what are resources that available and can be used to build EAC, and how to evaluate EAC performance