https://www.semanticscholar.org/paper/Design-Challenges-for-Entity-Linking-Ling-Singh/aa2a7ac7bfa9a0201d4faddd4e7bb26299a5e0be

06 - Design Challenges for Entity Linking

https://www.semanticscholar.org/paper/Capturing-Semantic-Similarity-for-Entity-Linking-w-Francis-Landau-Durrett/1c9aca60f7ac5edcceb73d612806704a7d662643

07 - Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

The recently-hyped paper that got "superhuman" performance on FAIR's CLEVR dataset.

https://arxiv.org/abs/1706.01427

20 - A simple neural network module for relational reasoning

EMNLP 2017 paper by Nitish Gupta, Sameer Singh, and Dan Roth.

Nitish comes on to talk to us about his paper, which presents a new entity linking model that both unifies prior sources of information into a single neural model, and trains that model in a domain-agnostic way, so it can be transferred to new domains without much performance degradation.

https://www.semanticscholar.org/paper/Entity-Linking-via-Joint-Encoding-of-Types-Descrip-Gupta-Singh/a66b6a3ac0aa9af6c178c1d1a4a97fd14a882353

33 - Entity Linking via Joint Encoding of Types, Descriptions, and Context, with Nitish Gupta

Paper by Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman.

Sam comes on to tell us about GLUE.  We talk about the motivation behind setting up a benchmark framework for natural language understanding, how the authors defined "NLU" and chose the tasks for this benchmark, a very nice diagnostic dataset that was constructed for GLUE, and what insight they gained from the experiments they've run so far.  We also have some musings about the utility of general-purpose sentence vectors, and about leaderboards.

https://www.semanticscholar.org/paper/GLUE%3A-A-Multi-Task-Benchmark-and-Analysis-Platform-Wang-Singh/a2054eff8b4efe0f1f53d88c08446f9492ae07c1

nlp

User 988922179