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Found 45 papers, 20 papers with code
“This is a Problem, Don’t You Agree?” Framing and Bias in Human Evaluation for Natural Language Generation
Despite recent efforts reviewing current human evaluation practices for natural language generation (NLG) research, the lack of reported question wording and potential for framing effects or cognitive biases influencing results has been widely overlooked.
PLAtE: A Large-scale Dataset for List Page Web Extraction
In this work, we introduce the PLAtE (Pages of Lists Attribute Extraction) dataset as a challenging new web extraction task.
Self-augmented Data Selection for Few-shot Dialogue Generation
The natural language generation (NLG) module in task-oriented dialogue systems translates structured meaning representations (MRs) into text responses, which has a great impact on users' experience as the human-machine interaction interface.
White-box Testing of NLP models with Mask Neuron Coverage
Recent literature has seen growing interest in using black-box strategies like CheckList for testing the behavior of NLP models.
Pathologies of Pre-trained Language Models in Few-shot Fine-tuning
Although adapting pre-trained language models with few examples has shown promising performance on text classification, there is a lack of understanding of where the performance gain comes from.
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors
The proposed stochastic function is sampled from a Gaussian process prior to (1) provide infinite number of joint Gaussian distributions of random context variables (diversity-promoting) and (2) explicitly model dependency between context variables (accurate-encoding).
Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation
Neural language models show vulnerability to adversarial examples which are semantically similar to their original counterparts with a few words replaced by their synonyms.
FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows
Hence, we propose segment act, an extension of dialog act from utterance level to segment level, and crowdsource a large-scale dataset for it.
Explaining Prediction Uncertainty of Pre-trained Language Models by Detecting Uncertain Words in Inputs
We adapt two perturbation-based post-hoc interpretation methods, Leave-one-out and Sampling Shapley, to identify words in inputs that cause the uncertainty in predictions.
Simple Text Detoxification by Identifying a Linear Toxic Subspace in Language Model Embeddings
We hypothesize the existence of a low-dimensional toxic subspace in the latent space of pre-trained language models, the existence of which suggests that toxic features follow some underlying pattern and are thus removable.
SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks
Transformer-based pre-trained language models boost the performance of open-domain dialogue systems.
Contextualizing Variation in Text Style Transfer Datasets
Text style transfer involves rewriting the content of a source sentence in a target style.
Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing
Via a small portion of word-level swaps, these adversarial perturbations aim to make the resulting text semantically and spatially similar to its seed input (therefore sharing similar interpretations).
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks
Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
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Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory
Most NLP datasets are manually labeled, so suffer from inconsistent labeling or limited size.
The Amazing World of Neural Language Generation
Neural Language Generation (NLG) {--} using neural network models to generate coherent text {--} is among the most promising methods for automated text creation.
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers
To build an interpretable neural text classifier, most of the prior work has focused on designing inherently interpretable models or finding faithful explanations.
A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing
In Text-to-SQL semantic parsing, selecting the correct entities (tables and columns) for the generated SQL query is both crucial and challenging; the parser is required to connect the natural language (NL) question and the SQL query to the structured knowledge in the database.
HittER: Hierarchical Transformers for Knowledge Graph Embeddings
Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block.
Pointwise Paraphrase Appraisal is Potentially Problematic
The prevailing approach for training and evaluating paraphrase identification models is constructed as a binary classification problem: the model is given a pair of sentences, and is judged by how accurately it classifies pairs as either paraphrases or non-paraphrases.
Reevaluating Adversarial Examples in Natural Language
State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a successful attack.
Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection
Experiments show the effectiveness of the proposed method in providing explanations that are both faithful to models and interpretable to humans.
Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation
Experiments show the proposed data augmentation methods significantly improve the explainability of both neural classifiers.
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation
Generating paraphrases from given sentences involves decoding words step by step from a large vocabulary.
Neural Text Generation in Stories Using Entity Representations as Context
We introduce an approach to neural text generation that explicitly represents entities mentioned in the text.
Dynamic Entity Representations in Neural Language Models
Understanding a long document requires tracking how entities are introduced and evolve over time.
Neural Discourse Structure for Text Categorization
We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization.
DyNet: The Dynamic Neural Network Toolkit
In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.
LSTM based Conversation Models
In this paper, we present a conversational model that incorporates both context and participant role for two-party conversations.
A Latent Variable Recurrent Neural Network for Discourse Relation Language Models
This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences.
Document Context Language Models
Text documents are structured on multiple levels of detail: individual words are related by syntax, but larger units of text are related by discourse structure.
Better Document-level Sentiment Analysis from RST Discourse Parsing
Discourse structure is the hidden link between surface features and document-level properties, such as sentiment polarity.
deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets
We introduce Discriminative BLEU (deltaBLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs.
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.
One Vector is Not Enough: Entity-Augmented Distributed Semantics for Discourse Relations
A more subtle challenge is that it is not enough to represent the meaning of each argument of a discourse relation, because the relation may depend on links between lowerlevel components, such as entity mentions.
Entity-Augmented Distributional Semantics for Discourse Relations
A more subtle challenge is that it is not enough to represent the meaning of each sentence of a discourse relation, because the relation may depend on links between lower-level elements, such as entity mentions.
One Vector is Not Enough: Entity-Augmented Distributional Semantics for Discourse Relations
Discourse relations bind smaller linguistic units into coherent texts.
Extracting Lexically Divergent Paraphrases from Twitter
We present MultiP (Multi-instance Learning Paraphrase Model), a new model suited to identify paraphrases within the short messages on Twitter.
