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: To simplify the task, we set up a binary classification: candidates who have been liked or shortlisted are considered part of the hirable class and others part of the not hirable class.
Question: How is "hirability" defined?

SOLUTION: candidates who have been liked or shortlisted are considered part of the hirable class

PROBLEM: Proposed Fusion Techniques ::: Step-Wise Decoder Fusion
Our first proposed technique is the step-wise decoder fusion of visual features during every prediction step i.e. we concatenate the visual encoding as context at each step of the decoding process. Proposed Fusion Techniques ::: Multimodal Attention Modulation
Similar to general attention BIBREF8, wherein a variable-length alignment vector $a_{th}(s)$, whose size equals the number of time steps on the source side, is derived by comparing the current target hidden state $h_{t}$ with each source hidden state $\overline{h_{s}}$; we consider a variant wherein the visual encoding $v_{t}$ is used to calculate an attention distribution $a_{tv}(s)$ over the source encodings as well. Proposed Fusion Techniques ::: Visual-Semantic (VS) Regularizer
In terms of leveraging the visual modality for supervision, BIBREF1 use multi-task learning to learn grounded representations through image representation prediction.
Question: What are 3 novel fusion techniques that are proposed?

SOLUTION: Step-Wise Decoder Fusion Multimodal Attention Modulation Visual-Semantic (VS) Regularizer

PROBLEM: We evaluate sentence-level semantics using averaged bag of vectors (BoV) representations on the Semantic Textual Similarity (STSB) task BIBREF21 and Word Content (WC) probing task (identify from a list of words which is contained in the sentence representation) from SentEval BIBREF22. Syntax: Similarly, we use the Google Syntactic analogies (GSyn) BIBREF9 to evaluate word-level syntactic information, and Depth (Dep) and Top Constituent (TopC) (of the input sentence's constituent parse tree) probing tasks from SentEval BIBREF22 for sentence-level syntax.
Question: What semantic and syntactic tasks are used as probes?

SOLUTION:
Word Content (WC) probing task Depth (Dep) and Top Constituent (TopC) (of the input sentence's constituent parse tree) probing tasks