Stanford MLab at SemEval-2021 Task 8: 48 Hours Is All You Need

2021-08-01 · SEMEVAL 2021 · Patrick Liu, Niveditha Iyer, Erik Rozi, Ethan A. Chi

This paper presents our system for the Quantity span identification, Unit of measurement identification and Value modifier classification subtasks of the MeasEval 2021 task. The purpose of the Quantity span identification task was to locate spans of text that contain a count or measurement, consisting of a value, usually followed by a unit and occasionally additional modifiers. The goal of the modifier classification task was to determine whether an associated text fragment served to indicate range, tolerance, mean value, etc. of a quantity. The developed systems used pre-trained BERT models which were fine-tuned for the task at hand. We present our system, investigate how architectural decisions affected model predictions, and conduct an error analysis. Overall, our system placed 12 / 19 in the shared task and in the 2nd place for the Unit subcategory.

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Multi-Head Attention 설명 없음

Attention 설명 없음

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Linear Warmup With Linear Decay Linear Warmup With Linear Decay is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.

Weight Decay 설명 없음

Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…

Residual Connection 설명 없음

Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings

2021-08-01 · SEMEVAL 2021 · Erik Rozi, Niveditha Iyer, Gordon Chi, Enok Choe 외

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 w…

Lexical Complexity Prediction Prediction Reading Comprehension Word Embeddings

Stanford MLab at SemEval 2022 Task 7: Tree- and Transformer-Based Methods for Clarification Plausibility

2022-07-01 · SemEval (NAACL) 2022 7 · Thomas Yim, Junha Lee, Rishi Verma, Scott Hickmann 외

In this paper, we detail the methods we used to determine the idiomaticity and plausibility of candidate words or phrases into an instructional text as part of the SemEval Task 7: Identifying Plausible Clarifications of …

Team Stanford ACMLab at SemEval 2022 Task 4: Textual Analysis of PCL Using Contextual Word Embeddings

2022-07-01 · SemEval (NAACL) 2022 7 · Upamanyu Dass-Vattam, Spencer Wallace, Rohan Sikand, Zach Witzel 외

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…

Binary Classification Word Embeddings

Stanford MLab at SemEval-2023 Task 10: Exploring GloVe- and Transformer-Based Methods for the Explainable Detection of Online Sexism

2023-05-07 · Hee Jung Choi, Trevor Chow, Aaron Wan, Hong Meng Yam 외

In this paper, we discuss the methods we applied at SemEval-2023 Task 10: Towards the Explainable Detection of Online Sexism. Given an input text, we perform three classification tasks to predict whether the text is sexi…

TMLab SRPOL at SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums

2019-05-29 · SEMEVAL 2019 6 · Piotr Niewinski, Aleksander Wawer, Maria Pszona, Maria Janicka

The article describes our submission to SemEval 2019 Task 8 on Fact-Checking in Community Forums. The systems under discussion participated in Subtask A: decide whether a question asks for factual information, opinion/ad…

Community Question Answering Fact Checking Question Answering Sentence