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Found 14 papers, 6 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.
Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing
We tackle this problem by introducing scenario-based semantic parsing: a variant of the original task which first requires disambiguating an utterance's "scenario" (an intent-slot template with variable leaf spans) before generating its frame, complete with ontology and utterance tokens.
Assessing Data Efficiency in Task-Oriented Semantic Parsing
Data efficiency, despite being an attractive characteristic, is often challenging to measure and optimize for in task-oriented semantic parsing; unlike exact match, it can require both model- and domain-specific setups, which have, historically, varied widely across experiments.
Diagnosing Transformers in Task-Oriented Semantic Parsing
Modern task-oriented semantic parsing approaches typically use seq2seq transformers to map textual utterances to semantic frames comprised of intents and slots.
Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing
An effective recipe for building seq2seq, non-autoregressive, task-oriented parsers to map utterances to semantic frames proceeds in three steps: encoding an utterance $x$, predicting a frame's length |y|, and decoding a |y|-sized frame with utterance and ontology tokens.
Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modeling
Task-oriented semantic parsing models typically have high resource requirements: to support new ontologies (i. e., intents and slots), practitioners crowdsource thousands of samples for supervised fine-tuning.
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.
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).
Accelerating Natural Language Understanding in Task-Oriented Dialog
Task-oriented dialog models typically leverage complex neural architectures and large-scale, pre-trained Transformers to achieve state-of-the-art performance on popular natural language understanding benchmarks.
Detecting Perceived Emotions in Hurricane Disasters
Natural disasters (e. g., hurricanes) affect millions of people each year, causing widespread destruction in their wake.
Calibration of Pre-trained Transformers
Pre-trained Transformers are now ubiquitous in natural language processing, but despite their high end-task performance, little is known empirically about whether they are calibrated.
Lightweight Convolutional Representations for On-Device Natural Language Processing
The increasing computational and memory complexities of deep neural networks have made it difficult to deploy them on low-resource electronic devices (e. g., mobile phones, tablets, wearables).
Evaluating Lottery Tickets Under Distributional Shifts
The Lottery Ticket Hypothesis suggests large, over-parameterized neural networks consist of small, sparse subnetworks that can be trained in isolation to reach a similar (or better) test accuracy.
Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis
Insightful findings in political science often require researchers to analyze documents of a certain subject or type, yet these documents are usually contained in large corpora that do not distinguish between pertinent and non-pertinent documents.
