57 citations · 109 across the 21 of their papers we have counts for
22 papers
Unsupervised Prototype Adapter for Vision-Language Models
Yi Zhang, Ce Zhang, Xueting Hu +1
Recently, large-scale pre-trained vision-language models (e.g. CLIP and ALIGN) have demonstrated remarkable effectiveness in acquiring transferable visual representations. To lever…
Backward Compatibility During Data Updates by Weight Interpolation
Raphael Schumann, Elman Mansimov, Yi-An Lai +3
Backward compatibility of model predictions is a desired property when updating a machine learning driven application. It allows to seamlessly improve the underlying model without…
Dialog2API: Task-Oriented Dialogue with API Description and Example Programs
Raphael Shu, Elman Mansimov, Tamer Alkhouli +6
Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing…
Injecting Domain Knowledge in Language Models for Task-Oriented Dialogue Systems
Denis Emelin, Daniele Bonadiman, Sawsan Alqahtani +2
Pre-trained language models (PLM) have advanced the state-of-the-art across NLP applications, but lack domain-specific knowledge that does not naturally occur in pre-training data.…
Unveiling Transformers with LEGO: a synthetic reasoning task
Yi Zhang, Arturs Backurs, Sébastien Bubeck +3
We propose a synthetic reasoning task, LEGO (Learning Equality and Group Operations), that encapsulates the problem of following a chain of reasoning, and we study how the Transfor…
Label Semantic Aware Pre-training for Few-shot Text Classification
Aaron Mueller, Jason Krone, Salvatore Romeo +4
In text classification tasks, useful information is encoded in the label names. Label semantic aware systems have leveraged this information for improved text classification perfor…
Clinical Prompt Learning with Frozen Language Models
Niall Taylor, Yi Zhang, Dan Joyce +2
Prompt learning is a new paradigm in the Natural Language Processing (NLP) field which has shown impressive performance on a number of natural language tasks with common benchmarki…
Dialogue Meaning Representation for Task-Oriented Dialogue Systems
Xiangkun Hu, Junqi Dai, Hang Yan +4
Dialogue meaning representation formulates natural language utterance semantics in their conversational context in an explicit and machine-readable form. Previous work typically fo…
Measuring and Reducing Model Update Regression in Structured Prediction for NLP
Deng Cai, Elman Mansimov, Yi-An Lai +3
Recent advance in deep learning has led to the rapid adoption of machine learning-based NLP models in a wide range of applications. Despite the continuous gain in accuracy, backwar…
Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings
Sawsan Alqahtani, Garima Lalwani, Yi Zhang +2
Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target emb…
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Yixuan Su, Lei Shu, Elman Mansimov +4
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascade…
Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder
Elman Mansimov, Yi Zhang
We introduce a Recursive INsertion-based Encoder (RINE), a novel approach for semantic parsing in task-oriented dialog. Our model consists of an encoder network that incrementally…
Alleviating the Knowledge-Language Inconsistency: A Study for Deep Commonsense Knowledge
Yi Zhang, Lei Li, Yunfang Wu +2
Knowledge facts are typically represented by relational triples, while we observe that some commonsense facts are represented by the triples whose forms are inconsistent with the e…
Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates
Yuqing Xie, Yi-an Lai, Yuanjun Xiong +2
Behavior of deep neural networks can be inconsistent between different versions. Regressions during model update are a common cause of concern that often over-weigh the benefits in…
Meta learning to classify intent and slot labels with noisy few shot examples
Shang-Wen Li, Jason Krone, Shuyan Dong +2
Recently deep learning has dominated many machine learning areas, including spoken language understanding (SLU). However, deep learning models are notorious for being data-hungry,…
Parallel Data Augmentation for Formality Style Transfer
Yi Zhang, Tao Ge, Xu Sun
The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data. In this paper, we study how to augment parallel data and propose novel and…
Learning to Classify Intents and Slot Labels Given a Handful of Examples
Jason Krone, Yi Zhang, Mona Diab
Intent classification (IC) and slot filling (SF) are core components in most goal-oriented dialogue systems. Current IC/SF models perform poorly when the number of training example…
Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections
Yi-An Lai, Xuan Zhu, Yi Zhang +1
Summarizing data samples by quantitative measures has a long history, with descriptive statistics being a case in point. However, as natural language processing methods flourish, t…
Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting
Yi Zhang, Tao Ge, Furu Wei +2
We study sequence-to-sequence (seq2seq) pre-training with data augmentation for sentence rewriting. Instead of training a seq2seq model with gold training data and augmented data s…
Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation
Yi-An Lai, Arshit Gupta, Yi Zhang
Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and negl…
Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency
Bingzhen Wei, Xuancheng Ren, Xu Sun +3
Abstractive text summarization is a highly difficult problem, and the sequence-to-sequence model has shown success in improving the performance on the task. However, the generated…
Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a Novel Neural Network
Jingjing Xu, Xu Sun
Recent studies have shown effectiveness in using neural networks for Chinese word segmentation. However, these models rely on large-scale data and are less effective for low-resour…