instruction:
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.
question:
Each passage was labelled by 3 unique annotators.
Question: How many annotators were there?
answer:
3

question:
We compare our model with the following state-of-the-art joint entity and relation extraction models:

(1) SPTree BIBREF4: This is an end-to-end neural entity and relation extraction model using sequence LSTM and Tree LSTM. (2) Tagging BIBREF5: This is a neural sequence tagging model which jointly extracts the entities and relations using an LSTM encoder and an LSTM decoder. (3) CopyR BIBREF6: This model uses an encoder-decoder approach for joint extraction of entities and relations. (4) HRL BIBREF11: This model uses a reinforcement learning (RL) algorithm with two levels of hierarchy for tuple extraction. (5) GraphR BIBREF14: This model considers each token in a sentence as a node in a graph, and edges connecting the nodes as relations between them. (6) N-gram Attention BIBREF9: This model uses an encoder-decoder approach with N-gram attention mechanism for knowledge-base completion using distantly supervised data.
Question: What is previous work authors reffer to?
answer:
SPTree Tagging CopyR HRL GraphR N-gram Attention

question:
We evaluate our method on IWSLT16 German-English (DE-EN) translation in both directions, WMT15 English-German (EN-DE) translation in both directions, and NIST Chinese-to-English (ZH$
ightarrow $EN) translation.
Question: Which languages do they experiment on?
answer:
German English Chinese