Topological Sort for Sentence Ordering
Sentence ordering is the task of arranging the sentences of a given text in the correct order. Recent work using deep neural networks for this task has framed it as a sequence prediction problem. In this paper, we propose a new framing of this task as a constraint solving problem and introduce a new technique to solve it. Additionally, we propose a human evaluation for this task. The results on both automatic and human metrics across four different datasets show that this new technique is better at capturing coherence in documents.
Code (2)
Tasks
SentenceSentence OrderingSimilar Papers 제목 키워드 기반
Formulating Neural Sentence Ordering as the Asymmetric Traveling Salesman Problem
The task of Sentence Ordering refers to rearranging a set of given sentences in a coherent ordering. Prior work (Prabhumoye et al., 2020) models this as an optimal graph traversal (with sentences as nodes, and edges as l…
Combinatorial OptimizationSentenceSentence OrderingTraveling Salesman ProblemOptimal Dynamic Treatment Regimes and Partial Welfare Ordering
Dynamic treatment regimes are treatment allocations tailored to heterogeneous individuals. The optimal dynamic treatment regime is a regime that maximizes counterfactual welfare. We introduce a framework in which we can …
counterfactualA New Sentence Ordering Method Using BERT Pretrained Model
Building systems with capability of natural language understanding (NLU) has been one of the oldest areas of AI. An essential component of NLU is to detect logical succession of events contained in a text. The task of se…
Natural Language UnderstandingSentenceSentence EmbeddingSentence-Embedding+2Positional Diffusion: Ordering Unordered Sets with Diffusion Probabilistic Models
Positional reasoning is the process of ordering unsorted parts contained in a set into a consistent structure. We present Positional Diffusion, a plug-and-play graph formulation with Diffusion Probabilistic Models to add…
Graph Neural NetworkSentenceSentence OrderingVisual StorytellingSequentially learning the topological ordering of causal directed acyclic graphs with likelihood ratio scores
Causal discovery, the learning of causality in a data mining scenario, has been of strong scientific and theoretical interest as a starting point to identify "what causes what?" Contingent on assumptions and a proper lea…
Causal Discovery