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:
The only remaining question is what makes two environments similar enough to infer the existence of a common category. There is, again, a large literature on this question (including the aforementioned language modeling, unsupervised parsing, and alignment work), but in the current work we will make use of a very simple criterion: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same.
Question: How do they determine similar environments for fragments in their data augmentation scheme?

Ex Output:
fragments are interchangeable if they occur in at least one lexical environment that is exactly the same

Ex Input:
word embedding, input encoder, alignment, aggregation, and prediction
Question: what is the architecture of the baseline model?

Ex Output:
word embedding, input encoder, alignment, aggregation, and prediction.

Ex Input:
WAS: The architecture used in BIBREF3 without the audio input. The decoder output Chinese character at each timestep. Others keep unchanged to the original implementation. LipCH-Net-seq: For a fair comparison, we use sequence-to-sequence with attention framework to replace the Connectionist temporal classification (CTC) loss BIBREF14 used in LipCH-Net BIBREF5 when converting picture to pinyin. CSSMCM-w/o video: To evaluate the necessity of video information when predicting tone, the video stream is removed when predicting tone and Chinese characters. In other word, video is only used when predicting the pinyin sequence. The tone is predicted from the pinyin sequence. Tone information and pinyin information work together to predict Chinese character.
Question: What was the previous state of the art model for this task?

Ex Output:
WAS LipCH-Net-seq CSSMCM-w/o video