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Found 24 papers, 14 papers with code
Contemporary NLP Modeling in Six Comprehensive Programming Assignments
We present a series of programming assignments, adaptable to a range of experience levels from advanced undergraduate to PhD, to teach students design and implementation of modern NLP systems.
Membership inference attack with relative decision boundary distance
However, since the decision boundary distance, which measures robustness, is strongly affected by the random initial image, the adversary may get opposite results even for the same input samples.
EEL: Efficiently Encoding Lattices for Reranking
Standard decoding approaches for conditional text generation tasks typically search for an output hypothesis with high model probability, but this may not yield the best hypothesis according to human judgments of quality.
Language Model Self-improvement by Reinforcement Learning Contemplation
We demonstrate that SIRLC can be applied to various NLP tasks, such as reasoning problems, text generation, and machine translation.
Best-$k$ Search Algorithm for Neural Text Generation
We first investigate the vanilla best-first search (BFS) algorithm and then propose the Best-$k$ Search algorithm.
Robust Unstructured Knowledge Access in Conversational Dialogue with ASR Errors
Knowledge cluster classification is boosted from 0. 7924 to 0. 9333 in Recall@1.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Multi-agent Dynamic Algorithm Configuration
MA-DAC formulates the dynamic configuration of a complex algorithm with multiple types of hyperparameters as a contextual multi-agent Markov decision process and solves it by a cooperative multi-agent RL (MARL) algorithm.
Neural Architecture Search on Efficient Transformers and Beyond
To address this issue, we propose a new framework to find optimal architectures for efficient Transformers with the neural architecture search (NAS) technique.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors
We compare performance of state-of-the-art factuality metrics, including recent ChatGPT-based metrics, on this stratified benchmark and show that their performance varies significantly across different types of summarization models.
Massive-scale Decoding for Text Generation using Lattices
Conditional neural text generation models generate high-quality outputs, but often concentrate around a mode when what we really want is a diverse set of options.
ASPECTNEWS: Aspect-Oriented Summarization of News Documents
Generic summaries try to cover an entire document and query-based summaries try to answer document-specific questions.
Training Dynamics for Text Summarization Models
Across different datasets (CNN/DM, XSum, MediaSum) and summary properties, such as abstractiveness and hallucination, we study what the model learns at different stages of its fine-tuning process.
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
Disfluencies is an under-studied topic in NLP, even though it is ubiquitous in human conversation.
Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution
Despite the prominence of neural abstractive summarization models, we know little about how they actually form summaries and how to understand where their decisions come from.
Compressive Summarization with Plausibility and Salience Modeling
Compressive summarization systems typically rely on a crafted set of syntactic rules to determine what spans of possible summary sentences can be deleted, then learn a model of what to actually delete by optimizing for content selection (ROUGE).
Understanding Neural Abstractive Summarization Models via Uncertainty
An advantage of seq2seq abstractive summarization models is that they generate text in a free-form manner, but this flexibility makes it difficult to interpret model behavior.
Discourse-Aware Neural Extractive Text Summarization
Recently BERT has been adopted for document encoding in state-of-the-art text summarization models.
Neural Extractive Text Summarization with Syntactic Compression
In this work, we present a neural model for single-document summarization based on joint extraction and syntactic compression.
Spherical Latent Spaces for Stable Variational Autoencoders
A hallmark of variational autoencoders (VAEs) for text processing is their combination of powerful encoder-decoder models, such as LSTMs, with simple latent distributions, typically multivariate Gaussians.
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Incorporating Discriminator in Sentence Generation: a Gibbs Sampling Method
Generating plausible and fluent sentence with desired properties has long been a challenge.
Family Shopping Recommendation System Using User Profile and Behavior Data
Specifically we propose a novel approach for family-based shopping recommendation system.
Knowledge Graph Representation with Jointly Structural and Textual Encoding
In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities.
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification
Recently, neural networks have achieved great success on sentiment classification due to their ability to alleviate feature engineering.
