Search Results for author:
Found 30 papers, 10 papers with code
RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining
Large-scale pretrained language models have achieved SOTA results on NLP tasks.
MovieChats: Chat like Humans in a Closed Domain
In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots.
Dual Context-Guided Continuous Prompt Tuning for Few-Shot Learning
Prompt-based paradigm has shown its competitive performance in many NLP tasks.
Unraveling the Mystery of Scaling Laws: Part I
However, the original scaling law paper by OpenAI did not disclose the complete details necessary to derive the precise scaling law formulas, and their conclusions are only based on models containing up to 1. 5 billion parameters.
SASFormer: Transformers for Sparsely Annotated Semantic Segmentation
In this work, we propose a simple yet effective sparse annotated semantic segmentation framework based on segformer, dubbed SASFormer, that achieves remarkable performance.
WeLM: A Well-Read Pre-trained Language Model for Chinese
Large Language Models pre-trained with self-supervised learning have demonstrated impressive zero-shot generalization capabilities on a wide spectrum of tasks.
Re-Attention Transformer for Weakly Supervised Object Localization
Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories.
Temporal Multimodal Multivariate Learning
We introduce temporal multimodal multivariate learning, a new family of decision making models that can indirectly learn and transfer online information from simultaneous observations of a probability distribution with more than one peak or more than one outcome variable from one time stage to another.
Knowledge-enhanced Session-based Recommendation with Temporal Transformer
We introduce time interval embedding to represent the time pattern between the item that needs to be predicted and historical click, and use it to replace the position embedding in the original transformer (called temporal transformer).
PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management
PatrickStar uses the CPU-GPU heterogeneous memory space to store the model data.
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
Recent advancements in open-domain question answering (ODQA), i. e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets.
Complementary Evidence Identification in Open-Domain Question Answering
This paper proposes a new problem of complementary evidence identification for open-domain question answering (QA).
Neural Data-to-Text Generation with LM-based Text Augmentation
Our approach automatically augments the data available for training by (i) generating new text samples based on replacing specific values by alternative ones from the same category, (ii) generating new text samples based on GPT-2, and (iii) proposing an automatic method for pairing the new text samples with data samples.
Patient-Specific Seizure Prediction Using Single Seizure Electroencephalography Recording
But these models require a considerable number of patient-specific seizures to be recorded for extracting the preictal and interictal EEG data for training a classifier.
Deep Structured Prediction for Facial Landmark Detection
Existing deep learning based facial landmark detection methods have achieved excellent performance.
Ranked #6 on
Facial Landmark Detection
on 300W
Frustratingly Hard Evidence Retrieval for QA Over Books
A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth.
Multimodal Dialogue State Tracking By QA Approach with Data Augmentation
Different from purely text-based dialogue state tracking, the dialogue in AVSD contains a sequence of question-answer pairs about a video and the final answer to the given question requires additional understanding of the video.
Diversifying Dialogue Generation with Non-Conversational Text
Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation.
Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence
The neural attention model has achieved great success in data-to-text generation tasks.
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems
We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML).
Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator
Pointer Generators have been the de facto standard for modern summarization systems.
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension
Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.
Select and Attend: Towards Controllable Content Selection in Text Generation
As a result, the content to be described in the text cannot be explicitly controlled.
Improving Multi-turn Dialogue Modelling with Utterance ReWriter
To properly train the utterance rewriter, we collect a new dataset with human annotations and introduce a Transformer-based utterance rewriting architecture using the pointer network.
NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation
Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems.
A Cost-Effective Framework for Preference Elicitation and Aggregation
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features.
Improving Variational Encoder-Decoders in Dialogue Generation
Variational encoder-decoders (VEDs) have shown promising results in dialogue generation.
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.
Toward Cognitive and Immersive Systems: Experiments in a Cognitive Microworld
These \textit{cognitive and immersive systems} (CAISs) fall squarely into the intersection of AI with HCI/HRI: such systems interact with and assist the human agents that enter them, in no small part because such systems are infused with AI able to understand and reason about these humans and their knowledge, beliefs, goals, communications, plans, etc.
A Conditional Variational Framework for Dialog Generation
Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems.
