Search Results for author:
Found 79 papers, 43 papers with code
Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer Approach
In this paper, we address unsupervised chunking as a new task of syntactic structure induction, which is helpful for understanding the linguistic structures of human languages as well as processing low-resource languages.
Improving Word Sense Disambiguation with Translations
It has been conjectured that multilingual information can help monolingual word sense disambiguation (WSD).
NeuZip: Memory-Efficient Training and Inference with Dynamic Compression of Neural Networks
However, the model sizes are constrained by the available on-device memory during training and inference.
LLMR: Knowledge Distillation with a Large Language Model-Induced Reward
Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) tasks.
A Dual-View Approach to Classifying Radiology Reports by Co-Training
Radiology report analysis provides valuable information that can aid with public health initiatives, and has been attracting increasing attention from the research community.
Tree-Averaging Algorithms for Ensemble-Based Unsupervised Discontinuous Constituency Parsing
We address unsupervised discontinuous constituency parsing, where we observe a high variance in the performance of the only previous model in the literature.
EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation
The ability of zero-shot translation emerges when we train a multilingual model with certain translation directions; the model can then directly translate in unseen directions.
Flora: Low-Rank Adapters Are Secretly Gradient Compressors
Despite large neural networks demonstrating remarkable abilities to complete different tasks, they require excessive memory usage to store the optimization states for training.
Ginger: An Efficient Curvature Approximation with Linear Complexity for General Neural Networks
The major reason is due to the quadratic memory and cubic time complexity to compute the inverse of the matrix.
Ensemble Distillation for Unsupervised Constituency Parsing
We investigate the unsupervised constituency parsing task, which organizes words and phrases of a sentence into a hierarchical structure without using linguistically annotated data.
Zero-Shot Continuous Prompt Transfer: Generalizing Task Semantics Across Language Models
Prompt tuning in natural language processing (NLP) has become an increasingly popular method for adapting large language models to specific tasks.
Weakly Supervised Reasoning by Neuro-Symbolic Approaches
Deep learning has largely improved the performance of various natural language processing (NLP) tasks.
Search and Learning for Unsupervised Text Generation
With the advances of deep learning techniques, text generation is attracting increasing interest in the artificial intelligence (AI) community, because of its wide applications and because it is an essential component of AI.
Unsupervised Chunking with Hierarchical RNN
Our approach involves a two-stage training process: pretraining with an unsupervised parser and finetuning on downstream NLP tasks.
f-Divergence Minimization for Sequence-Level Knowledge Distillation
Experiments across four datasets show that our methods outperform existing KD approaches, and that our symmetric distilling losses can better force the student to learn from the teacher distribution.
Prompt-Based Editing for Text Style Transfer
In this paper, we present a prompt-based editing approach for text style transfer.
Teacher Forcing Recovers Reward Functions for Text Generation
We additionally propose a simple modification to stabilize the RL training on non-parallel datasets with our induced reward function.
An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation
Open-domain dialogue systems aim to interact with humans through natural language texts in an open-ended fashion.
Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree Search
We use Monte Carlo Tree Search as a decoding mechanism to steer the probability distribution learned by a language model towards a given emotion.
Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization
Text summarization aims to generate a short summary for an input text.
A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization
Sentence summarization aims at compressing a long sentence into a short one that keeps the main gist, and has extensive real-world applications such as headline generation.
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing
Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences.
Ranked #5 on
Dialog Relation Extraction
on DialogRE
(F1c (v1) metric)
Dialog Relation Extraction
Document-level Relation Extraction
+2
MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration
In previous work, researchers have developed several calibration methods to post-process the outputs of a predictor to obtain calibrated values, such as binning and scaling methods.
An Empirical Study on the Overlapping Problem of Open-Domain Dialogue Datasets
Open-domain dialogue systems aim to converse with humans through text, and dialogue research has heavily relied on benchmark datasets.
Search and Learn: Improving Semantic Coverage for Data-to-Text Generation
To this end, we propose a search-and-learning approach that leverages pretrained language models but inserts the missing slots to improve the semantic coverage.
Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision
How do we perform efficient inference while retaining high translation quality?
Simulated annealing for optimization of graphs and sequences
The key idea is to integrate powerful neural networks into metaheuristics (e. g., simulated annealing, SA) to restrict the search space in discrete optimization.
Simulated Annealing for Emotional Dialogue Systems
Explicitly modeling emotions in dialogue generation has important applications, such as building empathetic personal companions.
Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic
In this work, we address the explainability of NLI by weakly supervised logical reasoning, and propose an Explainable Phrasal Reasoning (EPR) approach.
Semi-Supervised and Unsupervised Sense Annotation via Translations
Our two unsupervised methods refine sense annotations produced by a knowledge-based WSD system via lexical translations in a parallel corpus.
A Globally Normalized Neural Model for Semantic Parsing
In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing.
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model
Multi-label emotion classification is an important task in NLP and is essential to many applications.
Generalized Equivariance and Preferential Labeling for GNN Node Classification
Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content.
Formality Style Transfer with Shared Latent Space
Conventional approaches for formality style transfer borrow models from neural machine translation, which typically requires massive parallel data for training.
Unsupervised Text Generation by Learning from Search
In this work, we present TGLS, a novel framework to unsupervised Text Generation by Learning from Search.
Stylized Text Generation: Approaches and Applications
We start from the definition of style and different settings of stylized text generation, illustrated with various applications.
Iterative Edit-Based Unsupervised Sentence Simplification
We present a novel iterative, edit-based approach to unsupervised sentence simplification.
Ranked #5 on
Text Simplification
on Newsela
Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction
Automatic sentence summarization produces a shorter version of a sentence, while preserving its most important information.
How Chaotic Are Recurrent Neural Networks?
Recurrent neural networks (RNNs) are non-linear dynamic systems.
TreeGen: A Tree-Based Transformer Architecture for Code Generation
TreeGen outperformed the previous state-of-the-art approach by 4. 5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89. 1%) and GEO (89. 6%).
Adversarial Learning on the Latent Space for Diverse Dialog Generation
Generating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation but also being able to generate fluent sentences during inference.
Stylized Text Generation Using Wasserstein Autoencoders with a Mixture of Gaussian Prior
Moreover, we can train our model on relatively small datasets and learn the latent representation of a specified class by adding external data with other styles/classes to our dataset.
Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer
Formality text style transfer plays an important role in various NLP applications, such as non-native speaker assistants and child education.
Unsupervised Paraphrasing by Simulated Annealing
Unsupervised paraphrase generation is a promising and important research topic in natural language processing.
Generating Sentences from Disentangled Syntactic and Semantic Spaces
In this paper, we propose to generate sentences from disentangled syntactic and semantic spaces.
An Imitation Learning Approach to Unsupervised Parsing
In our work, we propose an imitation learning approach to unsupervised parsing, where we transfer the syntactic knowledge induced by the PRPN to a Tree-LSTM model with discrete parsing actions.
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
In the natural language processing literature, neural networks are becoming increasingly deeper and complex.
Ranked #60 on
Sentiment Analysis
on SST-2 Binary classification
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling
In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements.
A Grammar-Based Structural CNN Decoder for Code Generation
In this paper, we propose a grammar-based structural convolutional neural network (CNN) for code generation.
Progressive Memory Banks for Incremental Domain Adaptation
The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the number of parameters, and thus the model capacity.
Disentangled Representation Learning for Non-Parallel Text Style Transfer
This paper tackles the problem of disentangling the latent variables of style and content in language models.
JUMPER: Learning When to Make Classification Decisions in Reading
Both the classification result and when to make the classification are part of the decision process, which is controlled by a policy network and trained with reinforcement learning.
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation
The variational autoencoder (VAE) imposes a probabilistic distribution (typically Gaussian) on the latent space and penalizes the Kullback--Leibler (KL) divergence between the posterior and prior.
Variational Attention for Sequence-to-Sequence Models
The variational encoder-decoder (VED) encodes source information as a set of random variables using a neural network, which in turn is decoded into target data using another neural network.
Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation
This paper addresses the question: Why do neural dialog systems generate short and meaningless replies?
Modeling Past and Future for Neural Machine Translation
The Past and Future contents are fed to both the attention model and the decoder states, which offers NMT systems the knowledge of translated and untranslated contents.
Affective Neural Response Generation
Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content.
Order-Planning Neural Text Generation From Structured Data
Generating texts from structured data (e. g., a table) is important for various natural language processing tasks such as question answering and dialog systems.
Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models
Neural network-based dialog systems are attracting increasing attention in both academia and industry.
How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models
Generative conversational systems are attracting increasing attention in natural language processing (NLP).
Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection
Speaker change detection (SCD) is an important task in dialog modeling.
RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
Open-domain human-computer conversation has been attracting increasing attention over the past few years.
Coupling Distributed and Symbolic Execution for Natural Language Queries
Building neural networks to query a knowledge base (a table) with natural language is an emerging research topic in deep learning.
Dialogue Session Segmentation by Embedding-Enhanced TextTiling
In human-computer conversation systems, the context of a user-issued utterance is particularly important because it provides useful background information of the conversation.
Compressing Neural Language Models by Sparse Word Representations
Such approaches are time- and memory-intensive because of the large numbers of parameters for word embeddings and the output layer.
Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years.
StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation
In this paper, we propose StalemateBreaker, a conversation system that can proactively introduce new content when appropriate.
How Transferable are Neural Networks in NLP Applications?
Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain.
Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation
However, existing neural networks for relation classification are usually of shallow architectures (e. g., one-layer convolutional neural networks or recurrent networks).
Ranked #2 on
Relation Classification
on SemEval 2010 Task 8
Natural Language Inference by Tree-Based Convolution and Heuristic Matching
In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction between two sentences.
Ranked #85 on
Natural Language Inference
on SNLI
Backward and Forward Language Modeling for Constrained Sentence Generation
Provided a specific word, we use RNNs to generate previous words and future words, either simultaneously or asynchronously, resulting in two model variants.
On End-to-End Program Generation from User Intention by Deep Neural Networks
This paper envisions an end-to-end program generation scenario using recurrent neural networks (RNNs): Users can express their intention in natural language; an RNN then automatically generates corresponding code in a characterby-by-character fashion.
A Comparative Study on Regularization Strategies for Embedding-based Neural Networks
This paper aims to compare different regularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP.
Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path
Relation classification is an important research arena in the field of natural language processing (NLP).
Ranked #4 on
Relation Classification
on SemEval 2010 Task 8
Distilling Word Embeddings: An Encoding Approach
Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems.
Discriminative Neural Sentence Modeling by Tree-Based Convolution
This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative sentence modeling.
Ranked #7 on
Text Classification
on TREC-6
Convolutional Neural Networks over Tree Structures for Programming Language Processing
Programming language processing (similar to natural language processing) is a hot research topic in the field of software engineering; it has also aroused growing interest in the artificial intelligence community.
Building Program Vector Representations for Deep Learning
In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis.
