13 citations · 34 across the 27 of their papers we have counts for
15 papers · 1 filter
High-Rank Structured Modulation for Parameter-Efficient Fine-Tuning
Yongkang Liu, Xing Li, Mengjie Zhao +7
As the number of model parameters increases, parameter-efficient fine-tuning (PEFT) has become the go-to choice for tailoring pre-trained large language models. Low-rank Adaptation…
ComperDial: Commonsense Persona-grounded Dialogue Dataset and Benchmark
Hiromi Wakaki, Yuki Mitsufuji, Yoshinori Maeda +5
We propose a new benchmark, ComperDial, which facilitates the training and evaluation of evaluation metrics for open-domain dialogue systems. ComperDial consists of human-scored re…
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao +4
We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing. Addressing such a task requires a system…
DiffuCOMET: Contextual Commonsense Knowledge Diffusion
Silin Gao, Mete Ismayilzada, Mengjie Zhao +3
Inferring contextually-relevant and diverse commonsense to understand narratives remains challenging for knowledge models. In this work, we develop a series of knowledge models, Di…
Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain
Alexandra DeLucia, Mengjie Zhao, Yoshinori Maeda +3
While valuable datasets such as PersonaChat provide a foundation for training persona-grounded dialogue agents, they lack diversity in conversational and narrative settings, primar…
On the Language Encoder of Contrastive Cross-modal Models
Mengjie Zhao, Junya Ono, Zhi Zhong +7
Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks. However, there has been limited investigation of and improvemen…
This joke is [MASK]: Recognizing Humor and Offense with Prompting
Junze Li, Mengjie Zhao, Yubo Xie +3
Humor is a magnetic component in everyday human interactions and communications. Computationally modeling humor enables NLP systems to entertain and engage with users. We investiga…
Modular and Parameter-Efficient Multimodal Fusion with Prompting
Sheng Liang, Mengjie Zhao, Hinrich Schütze
Recent research has made impressive progress in large-scale multimodal pre-training. In the context of the rapid growth of model size, it is necessary to seek efficient and flexibl…
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework
Mengjie Zhao, Fei Mi, Yasheng Wang +4
Vast efforts have been devoted to creating high-performance few-shot learners, i.e., large-scale pretrained language models (PLMs) that perform well with little downstream task tra…
Discrete and Soft Prompting for Multilingual Models
Mengjie Zhao, Hinrich Schütze
It has been shown for English that discrete and soft prompting perform strongly in few-shot learning with pretrained language models (PLMs). In this paper, we show that discrete an…
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters
Mengjie Zhao, Yi Zhu, Ehsan Shareghi +4
Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no…
Continual Learning for Natural Language Generation in Task-oriented Dialog Systems
Fei Mi, Liangwei Chen, Mengjie Zhao +2
Natural language generation (NLG) is an essential component of task-oriented dialog systems. Despite the recent success of neural approaches for NLG, they are typically developed i…
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models
Mengjie Zhao, Tao Lin, Fei Mi +2
We present an efficient method of utilizing pretrained language models, where we learn selective binary masks for pretrained weights in lieu of modifying them through finetuning. E…
Quantifying the Contextualization of Word Representations with Semantic Class Probing
Mengjie Zhao, Philipp Dufter, Yadollah Yaghoobzadeh +1
Pretrained language models have achieved a new state of the art on many NLP tasks, but there are still many open questions about how and why they work so well. We investigate the c…
Multilingual Embeddings Jointly Induced from Contexts and Concepts: Simple, Strong and Scalable
Philipp Dufter, Mengjie Zhao, Hinrich Schütze
Word embeddings induced from local context are prevalent in NLP. A simple and effective context-based multilingual embedding learner is Levy et al. (2017)'s S-ID (sentence ID) meth…