TASK 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.
PROBLEM: Our semi-supervised approach is quite straightforward: first a model is trained on the training set and then this model is used to predict the labels of the silver data. This silver data is then simply added to our training set, after which the model is retrained. However, an extra step is applied to ensure that the silver data is of reasonable quality.
Question: What semi-supervised learning is applied?

SOLUTION: first a model is trained on the training set and then this model is used to predict the labels of the silver data This silver data is then simply added to our training set, after which the model is retrained

PROBLEM: 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.
Question: What is the main contribution of the paper?

SOLUTION: Proposing an improved RNN model, the phonetic temporal neural LID approach, based on phonetic features that results in better performance

PROBLEM: We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27.
Question: How big is seed lexicon used for training?

SOLUTION:
30 words