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.
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Question: Impact votes are provided by the users of the platform to evaluate how impactful a particular claim is. Users can pick one of 5 possible impact labels for a particular claim: no impact, low impact, medium impact, high impact and very high impact. While evaluating the impact of a claim, users have access to the full argument context and therefore, they can assess how impactful a claim is in the given context of an argument.
Question: What annotations are available in the dataset?

Answer: 5 possible impact labels for a particular claim: no impact, low impact, medium impact, high impact and very high impact

Question: Parallel Scan Inference
The commutative properties of semiring algorithms allow flexibility in the order in which we compute $A(\ell )$. Typical implementations of dynamic programming algorithms are serial in the length of the sequence. Vectorized Parsing
Computational complexity is even more of an issue for parsing algorithms, which cannot be as easily parallelized. Semiring Matrix Operations
The two previous optimizations reduce most of the cost to semiring matrix multiplication. In the specific case of the $(\sum , imes )$ semiring these can be computed very efficiently using matrix multiplication, which is highly-tuned on GPU hardware. Unfortunately for other semirings, such as log and max, these operations are either slow or very memory inefficient. For instance, for matrices $T$ and $U$ of sized $N imes M$ and $M imes O$, we can broadcast with $\otimes $ to a tensor of size $N imes M imes O$ and then reduce dim $M$ by $igoplus $ at a huge memory cost.
Question: What baselines are used in experiments?

Answer: Typical implementations of dynamic programming algorithms are serial in the length of the sequence Computational complexity is even more of an issue for parsing algorithms, which cannot be as easily parallelized Unfortunately for other semirings, such as log and max, these operations are either slow or very memory inefficient

Question: GhostVLAD is an extension of the NetVLAD approach, which we discussed in the previous section. GhostVLAD works exactly similar to NetVLAD except it adds Ghost clusters along with the NetVLAD clusters. So, now we will have a K+G number of clusters instead of K clusters. The Ghost clusters are added to map any noisy or irrelevant content into ghost clusters and are not included during the feature aggregation stage, as shown in Figure 1 (Right side).
Question: What is the GhostVLAD approach?

Answer:
extension of the NetVLAD adds Ghost clusters along with the NetVLAD clusters