Search Results for author:
Found 13 papers, 8 papers with code
CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions
This paper introduces CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, and recipes, from which knowledge covering a broad range of aspects is acquired.
Analysis of Multi-Source Language Training in Cross-Lingual Transfer
The successful adaptation of multilingual language models (LMs) to a specific language-task pair critically depends on the availability of data tailored for that condition.
RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer
To perform this task, we developed RecipeMind, a food affinity score prediction model that quantifies the suitability of adding an ingredient to set of other ingredients.
Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction
With the recent success and popularity of pre-trained language models (LMs) in natural language processing, there has been a rise in efforts to understand their inner workings.
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching
We present a latent variable model for predicting the relationship between a pair of text sequences.
SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification
We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem.
Ranked #6 on
Emotion Recognition in Conversation
on EC
SNU_IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification
We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem.
Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag Representations
Most existing recursive neural network (RvNN) architectures utilize only the structure of parse trees, ignoring syntactic tags which are provided as by-products of parsing.
Cell-aware Stacked LSTMs for Modeling Sentences
We propose a method of stacking multiple long short-term memory (LSTM) layers for modeling sentences.
Ranked #10 on
Sentiment Analysis
on SST-5 Fine-grained classification
Element-wise Bilinear Interaction for Sentence Matching
When we build a neural network model predicting the relationship between two sentences, the most general and intuitive approach is to use a Siamese architecture, where the sentence vectors obtained from a shared encoder is given as input to a classifier.
SNU\_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension
We present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018.
SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension
We present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018.
Learning to Compose Task-Specific Tree Structures
For years, recursive neural networks (RvNNs) have been shown to be suitable for representing text into fixed-length vectors and achieved good performance on several natural language processing tasks.
Ranked #62 on
Natural Language Inference
on SNLI
