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: The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module. We use a triplet network BIBREF41 , BIBREF42 in our representation module. The triplet network consists of three sub-parts: target, supporting, and contrasting networks. All three networks share the same parameters. The Mixture module brings the image and caption embeddings to a joint feature embedding space.
Question: How do the authors define a differential network?

SOLUTION: The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module.

PROBLEM: We evaluate our newly proposed models and related baselines in several low-resource settings across different languages with real, distantly supervised data with non-synthetic noise. The advanced modeling of the noisy labels substantially improves the performance up to 36% over methods without noise-handling and up to 9% over all other noise-handling baselines.
Question: Did they evaluate against baseline?

SOLUTION: Yes

PROBLEM: We build on the state-of-the-art publicly available question answering system by docqa. The system extends BiDAF BIBREF4 with self-attention and performs well on document-level QA.
Question: What is the underlying question answering algorithm?

SOLUTION:
The system extends BiDAF BIBREF4 with self-attention