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.

Q: 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?

A: English, German, Spanish, Italian, Japanese and Portuguese  English, Arabic and Chinese
****
Q: Several recent works have investigated jointly training the acoustic model with a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13, but these works did not evaluate their system on speech enhancement metrics. Indeed, our internal experiments show that without access to the clean data, joint training severely harms performance on these metrics. 
 Question: Which frozen acoustic model do they use?

A: a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13
****
Q: We can see that how 1.10intelligence is used varies by field: in computer science the most similar words include 1.10artificial and 1.10ai; in finance, similar words include 1.10abilities and 1.10consciousness. 
 Question: Which words are used differently across ArXiv?

A:
intelligence
****