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Reading Group Blog -- Semantically Equivalent Adversarial Rules for Debugging NLP Models (ACL 2018) February 19, 2019
In the second post, we will focus on this paper: Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "Semantically equivalent adversarial rules for debugging nlp models." Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vol. 1. 2018. Robustness is a central concern in engineering. Our suspension bridges […]
Reading Group Blog -- LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better (ACL 2018) January 25, 2019
Welcome to the Stanford NLP Reading Group Blog! Inspired by other groups, notably the UC Irvine NLP Group, we have decided to blog about the papers we read at our reading group. In this first post, we'll discuss the following paper: Kuncoro et al. "LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them […]
A New Multi-Turn, Multi-Domain, Task-Oriented Dialogue Dataset July 3, 2017
table { font-family: arial, sans-serif; border-collapse: collapse; width: 100%; } td, th { border: 1px solid #dddddd; text-align: left; padding: 8px; } tr:nth-child(even) { background-color: #dddddd; } Task-oriented dialogue focuses on conversational agents that participate in user-initiated dialogues on domain-specific topics. Traditionally, the task-oriented dialogue community has often been hindered by a lack of sufficiently […]
CS224n Competition on The Stanford Question Answering Dataset with CodaLab April 27, 2017
The Stanford Question Answering Dataset (SQuAD) is a reading comprehension benchmark with an active and highly-competitive leaderboard. Over 17 industry and academic teams have submitted their models (with executable code) since SQuAD’s release in June 2016, leading to the advancement of novel deep learning architectures which have outperformed baseline models by wide margins. As teams […]
Interactive Language Learning December 14, 2016
Today, natural language interfaces (NLIs) on computers or phones are often trained once and deployed, and users must just live with their limitations. Allowing users to demonstrate or teach the computer appears to be a central component to enable more natural and usable NLIs. Examining language acquisition research, there is considerable evidence suggesting that human […]

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