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: Experiments on two publicly available datasets (Camrest BIBREF11 and InCar Assistant BIBREF6) confirm the effectiveness of the KB-retriever.
Question: Which dialog datasets did they experiment with?

SOLUTION: Camrest InCar Assistant

PROBLEM: In order to represent individual sentences, we insert external [cls] tokens at the start of each sentence, and each [cls] symbol collects features for the sentence preceding it. We also use interval segment embeddings to distinguish multiple sentences within a document. This way, document representations are learned hierarchically where lower Transformer layers represent adjacent sentences, while higher layers, in combination with self-attention, represent multi-sentence discourse. Position embeddings in the original Bert model have a maximum length of 512; we overcome this limitation by adding more position embeddings that are initialized randomly and fine-tuned with other parameters in the encoder.
Question: What is novel about their document-level encoder?

SOLUTION: Bert model have a maximum length of 512; we overcome this limitation by adding more position embeddings we insert external [cls] tokens at the start of each sentence, and each [cls] symbol collects features for the sentence preceding it document representations are learned hierarchically

PROBLEM: Inspired by BIBREF12, we integrate in this paper a boundary assembling step into the state-of-the-art LSTM model for Chinese word segmentation, and feed the output into a CRF model for NER, resulting in a 2% absolute improvement on the overall F1 score over current state-of-the-art methods.
Question: What state-of-the-art deep neural network is used?

SOLUTION:
LSTM model