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Found 20 papers, 2 papers with code
Learning to Generate Examples for Semantic Processing Tasks
Experiments in low resource settings show that augmenting the training material with the proposed strategy systematically improves the results on text classification and natural language inference tasks by up to 10% accuracy, outperforming existing DA approaches.
Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings
Conversational Task Assistants (CTAs) are conversational agents whose goal is to help humans perform real-world tasks.
Leveraging Interesting Facts to Enhance User Engagement with Conversational Interfaces
Conversational Task Assistants (CTAs) guide users in performing a multitude of activities, such as making recipes.
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data
Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks.
Evaluation Metrics of Language Generation Models for Synthetic Traffic Generation Tasks
Experiments on three tasks, i. e., Shopping Utterance Generation, Product Question Generation and Query Auto Completion, demonstrate that our metrics are effective for evaluating STG tasks, and improve the agreement with human judgement up to 20% with respect to common NLG metrics.
Follow-on Question Suggestion via Voice Hints for Voice Assistants
Using a new dataset of 6681 input questions and human written hints, we evaluated the models with automatic metrics and human evaluation.
Preventing Catastrophic Forgetting in Continual Learning of New Natural Language Tasks
In this paper, we approach the problem of incrementally expanding MTL models' capability to solve new tasks over time by distilling the knowledge of an already trained model on n tasks into a new one for solving n+1 tasks.
Alexa, Let's Work Together: Introducing the First Alexa Prize TaskBot Challenge on Conversational Task Assistance
Since its inception in 2016, the Alexa Prize program has enabled hundreds of university students to explore and compete to develop conversational agents through the SocialBot Grand Challenge.
Learning to Solve NLP Tasks in an Incremental Number of Languages
In real scenarios, a multilingual model trained to solve NLP tasks on a set of languages can be required to support new languages over time.
VoiSeR: A New Benchmark for Voice-Based Search Refinement
This common user intent is usually available through a {``}filter-by{''} interface on online shopping websites, but is challenging to support naturally via voice, as the intent of refinements must be interpreted in the context of the original search, the initial results, and the available product catalogue facets.
GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples
Recent Transformer-based architectures, e. g., BERT, provide impressive results in many Natural Language Processing tasks.
Almawave-SLU: A new dataset for SLU in Italian
The widespread use of conversational and question answering systems made it necessary to improve the performances of speaker intent detection and understanding of related semantic slots, i. e., Spoken Language Understanding (SLU).
Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model
Moreover, we annotated a new dataset for the Italian language, and we observed similar performances without the need for changing the model.
Deep Learning in Semantic Kernel Spaces
Kernel methods enable the direct usage of structured representations of textual data during language learning and inference tasks.
A Language Independent Method for Generating Large Scale Polarity Lexicons
Sentiment Analysis systems aims at detecting opinions and sentiments that are expressed in texts.
HuRIC: a Human Robot Interaction Corpus
Recent years show the development of large scale resources (e. g. FrameNet for the Frame Semantics) that supported the definition of several state-of-the-art approaches in Natural Language Processing.
