Jillian Tang


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Team Stanford ACMLab at SemEval 2022 Task 4: Textual Analysis of PCL Using Contextual Word Embeddings
Upamanyu Dass-Vattam | Spencer Wallace | Rohan Sikand | Zach Witzel | Jillian Tang
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

We propose the use of a contextual embedding based-neural model on strictly textual inputs to detect the presence of patronizing or condescending language (PCL). We finetuned a pre-trained BERT model to detect whether or not a paragraph contained PCL (Subtask 1), and furthermore finetuned another pre-trained BERT model to identify the linguistic techniques used to convey the PCL (Subtask 2). Results show that this approach is viable for binary classification of PCL, but breaks when attempting to identify the PCL techniques. Our system placed 32/79 for subtask 1, and 40/49 for subtask 2.

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Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent
Ethan A. Chi | Ashwin Paranjape | Abigail See | Caleb Chiam | Trenton Chang | Kathleen Kenealy | Swee Kiat Lim | Amelia Hardy | Chetanya Rastogi | Haojun Li | Alexander Iyabor | Yutong He | Hari Sowrirajan | Peng Qi | Kaushik Ram Sadagopan | Nguyet Minh Phu | Dilara Soylu | Jillian Tang | Avanika Narayan | Giovanni Campagna | Christopher Manning
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

We present Chirpy Cardinal, an open-domain social chatbot. Aiming to be both informative and conversational, our bot chats with users in an authentic, emotionally intelligent way. By integrating controlled neural generation with scaffolded, hand-written dialogue, we let both the user and bot take turns driving the conversation, producing an engaging and socially fluent experience. Deployed in the fourth iteration of the Alexa Prize Socialbot Grand Challenge, Chirpy Cardinal handled thousands of conversations per day, placing second out of nine bots with an average user rating of 3.58/5.


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Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings
Erik Rozi | Niveditha Iyer | Gordon Chi | Enok Choe | Kathy J. Lee | Kevin Liu | Patrick Liu | Zander Lack | Jillian Tang | Ethan A. Chi
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper presents our system for the single- and multi-word lexical complexity prediction tasks of SemEval Task 1: Lexical Complexity Prediction. Text comprehension depends on the reader’s ability to understand the words present in it; evaluating the lexical complexity of such texts can enable readers to find an appropriate text and systems to tailor a text to an audience’s needs. We present our model pipeline, which applies a combination of embedding-based and manual features to predict lexical complexity on the CompLex English dataset using various tree-based and linear models. Our method is ranked 27 / 54 on single-word prediction and 14 / 37 on multi-word prediction.