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:
Our in-house dataset includes manually annotated RE data for 6 languages: English, German, Spanish, Italian, Japanese and Portuguese. The ACE05 dataset includes manually annotated RE data for 3 languages: English, Arabic and Chinese. It defines 7 entity types (Person, Organization, Geo-Political Entity, Location, Facility, Weapon, Vehicle) and 6 relation types between the entities (Agent-Artifact, General-Affiliation, ORG-Affiliation, Part-Whole, Personal-Social, Physical).
Question: What languages do they experiment on?
answer:
English, German, Spanish, Italian, Japanese and Portuguese English, Arabic and Chinese

question:
Similarly the agents utterances can be clustered to identify system responses. However, we argue that rather than treating user utterances and agents responses in an isolated manner, there is merit in jointly clustering them. There is adjacency information of these utterances that can be utilized to identify better user intents and system responses.
Question: Do they study frequent user responses to help automate modelling of those?
answer:
Yes

question:
However, we found that explicitly initializing these embeddings with vectors pre-trained over a large collection of unlabeled data significantly improved performance (see Section "Effects of initialized embeddings and corrupt-sampling schemes" ).
Question: What is the new initialization method proposed in this paper?
answer:
They initialize their word and entity embeddings with vectors pre-trained over a large corpus of unlabeled data.