Search Results for author:
Found 41 papers, 17 papers with code
UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor
However, the trained scoring model is prone to under-fitting for low-resource settings, as it relies on the training data.
Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text
In this paper, we propose to learn a prototype encoder from relation definition in a way that is useful for relation instance classification.
CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval
Conversational search is a difficult task as it aims at retrieving documents based not only on the current user query but also on the full conversation history.
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2. 0, focusing on the use of pre-trained language models (PLMs).
Ranked #1 on
Style Transfer
on GYAFC
Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring
In this paper, we propose a novel approach to extract and represent essay coherence features with prompt-learning NSP that shows to match the state-of-the-art AES coherence model, and achieves the best performance for long essays.
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation
However, NAR models usually generate texts of lower quality due to the absence of token dependency in the output text.
From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking
In this work, we propose a curriculum learning framework for context-aware document ranking, in which the ranking model learns matching signals between the search context and the candidate document in an easy-to-hard manner.
PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN
Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability).
Self-supervised Context-aware Style Representation for Expressive Speech Synthesis
In this paper, we propose a novel framework for learning style representation from abundant plain text in a self-supervised manner.
Learning to Transfer Prompts for Text Generation
First, PTG learns a set of source prompts for various source generation tasks and then transfers these prompts as target prompts to perform target generation tasks.
Pretrained Language Models for Text Generation: A Survey
We begin with introducing three key aspects of applying PLMs to text generation: 1) how to encode the input into representations preserving input semantics which can be fused into PLMs; 2) how to design an effective PLM to serve as the generation model; and 3) how to effectively optimize PLMs given the reference text and to ensure that the generated texts satisfy special text properties.
Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences.
Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals
The final response is selected according to the predicted knowledge, the goal to achieve, and the context.
Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment
Deep learning models for automatic readability assessment generally discard linguistic features traditionally used in machine learning models for the task.
The DELICES project: Indexing scientific literature through semantic expansion
Scientific digital libraries play a critical role in the development and dissemination of scientific literature.
Inductive Topic Variational Graph Auto-Encoder for Text Classification
T-VGAE inherits the interpretability of the topic model and the efficient information propagation mechanism of VGAE.
Representation Learning
Semi-Supervised Text Classification
+1
BERT4SO: Neural Sentence Ordering by Fine-tuning BERT
Sentence ordering aims to arrange the sentences of a given text in the correct order.
Neural Sentence Ordering Based on Constraint Graphs
Our experiments on five benchmark datasets show that our method outperforms all the existing baselines significantly, achieving a new state-of-the-art performance.
Content Selection Network for Document-grounded Retrieval-based Chatbots
It is thus crucial to select the part of document content relevant to the current conversation context.
Meta-Learning for Neural Relation Classification with Distant Supervision
Distant supervision provides a means to create a large number of weakly labeled data at low cost for relation classification.
Open-Domain Dialogue Generation Based on Pre-trained Language Models
These results show that discrepancies is an important factor to consider when we use a pre-trained model, and a reduction in discrepancies can lead to improved performance.
Jointly Optimizing State Operation Prediction and Value Generation for Dialogue State Tracking
However, in such a stacked encoder-decoder structure, the operation prediction objective only affects the BERT encoder and the value generation objective mainly affects the RNN decoder.
Ranked #2 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
A Simple and Efficient Multi-Task Learning Approach for Conditioned Dialogue Generation
Conditioned dialogue generation suffers from the scarcity of labeled responses.
Multi-Domain Dialogue State Tracking based on State Graph
Existing approaches usually concatenate previous dialogue state with dialogue history as the input to a bi-directional Transformer encoder.
Ranked #5 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
ScriptWriter: Narrative-Guided Script Generation
In dialogue systems, it would also be useful to drive dialogues by a dialogue plan.
VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification
Much progress has been made recently on text classification with methods based on neural networks.
Personalizing Search Results Using Hierarchical RNN with Query-aware Attention
Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training.
An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation
This paper studies graph-based recommendation, where an interaction graph is constructed from historical records and is lever-aged to alleviate data sparsity and cold start problems.
Ranked #1 on
Click-Through Rate Prediction
on MovieLens 1M
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases
Furthermore, we propose a diversified point network to generate a set of diverse keyphrases out of the word graph in the decoding process.
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.
Ranked #2 on
Link Prediction
on FB122
A Long-Short Demands-Aware Model for Next-Item Recommendation
Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so as to maximize their values.
Empirical Study of Multi-level Convolution Models for IR Based on Representations and Interactions
Deep learning models have been employed to perform IR tasks and have shown competitive results.
Image Inspired Poetry Generation in XiaoIce
This is one of the few attempts to generate poetry from images.
Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection
The system aims at exploring the potential of context information of terms for emotion analysis.
An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems.
Human-computer conversation systems have attracted much attention in Natural Language Processing.
A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
Our novel hierarchical recurrent encoder-decoder architecture allows the model to be sensitive to the order of queries in the context while avoiding data sparsity.
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations.
A General SIMD-based Approach to Accelerating Compression Algorithms
Compression algorithms are important for data oriented tasks, especially in the era of Big Data.
