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:
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:
The input document/summary that may have unordered sentences is processed so that it will have sentences clustered together. To test our approach, we jumble the ordering of sentences in a document, process the unordered document and compare the similarity of the output document with the original document. To structure an unordered document is an essential task in many applications. It is a post-requisite for applications like multiple document extractive text summarization where we have to present a summary of multiple documents. It is a prerequisite for applications like question answering from multiple documents where we have to present an answer by processing multiple documents.
Question: What is an unordered text document, do these arise in real-world corpora?
answer:
A unordered text document is one where sentences in the document are disordered or jumbled. It doesn't appear that unordered text documents appear in corpora, but rather are introduced as part of processing pipeline.

question:
We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al.
Question: What dataset is used?
answer:
HEOT A labelled dataset for a corresponding english tweets