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.

Q: In this work, we use the datasets released by BIBREF1 and HEOT dataset provided by BIBREF0 . Both the HEOT and BIBREF1 datasets contain tweets which are annotated in three categories: offensive, abusive and none (or benign)

A: Do they perform some annotation?
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Q: The basic concept is to use a phone-discriminative model to produce frame-level phonetic features, and then use these features to enhance RNN LID systems that were originally built with raw acoustic features. The initial step is therefore feature combination, with the phonetic feature used as auxiliary information to assist acoustic RNN LID. This is improved further, as additional research identified that a simpler model using only the phonetic feature as the RNN LID input provides even better performance. We call this RNN model based on phonetic features the phonetic temporal neural LID approach, or PTN LID. As well as having a simplified model structure, the PTN offers deeper insight into the LID task by rediscovering the value of the phonetic temporal property in language discrimination.

A: What is the main contribution of the paper?
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Q: We treat each individual language as a task and train a joint model for all the tasks. We seek to learn a set of shared character embeddings for taggers in both languages together through optimization of a joint loss function that combines the high-resource tagger and the low-resource one.

A:
How are character representations from various languages joint?
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