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Found 25 papers, 7 papers with code
NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails
NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
Dataset for a Neural Natural Language Interface for Databases (NNLIDB)
Progress in natural language interfaces to databases (NLIDB) has been slow mainly due to linguistic issues (such as language ambiguity) and domain portability.
Improving Retrieval-Based Question Answering with Deep Inference Models
Question answering is one of the most important and difficult applications at the border of information retrieval and natural language processing, especially when we talk about complex science questions which require some form of inference to determine the correct answer.
A Sketch-Based Neural Model for Generating Commit Messages from Diffs
In this paper we apply neural machine translation (NMT) techniques to convert code diffs into commit messages and we present an improved sketch-based encoder for this task.
Multimodal Semi-supervised Learning for Disaster Tweet Classification
In this paper, we investigate how to leverage the copious amounts of unlabeled data generated on social media by disaster eyewitnesses and affected individuals during disaster events.
Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay
This paper introduces a novel method for learning how to play the most difficult Atari 2600 games from the Arcade Learning Environment using deep reinforcement learning.
Neural Paraphrase Generation using Transfer Learning
Progress in statistical paraphrase generation has been hindered for a long time by the lack of large monolingual parallel corpora.
Natural Language Interface for Databases Using a Dual-Encoder Model
We propose a sketch-based two-step neural model for generating structured queries (SQL) based on a user{'}s request in natural language.
Answering questions by learning to rank -- Learning to rank by answering questions
Answering multiple-choice questions in a setting in which no supporting documents are explicitly provided continues to stand as a core problem in natural language processing.
Answering questions by learning to rank - Learning to rank by answering questions
Answering multiple-choice questions in a setting in which no supporting documents are explicitly provided continues to stand as a core problem in natural language processing.
Cross-Domain Training for Goal-Oriented Conversational Agents
Goal-Oriented Chatbots in fields such as customer support, providing certain information or general help with bookings or reservations, suffer from low performance partly due to the difficulty of obtaining large domain-specific annotated datasets.
A self-supervised neural-analytic method to predict the evolution of COVID-19 in Romania
More specifically, we want to statistically estimate all the relevant parameters for the new coronavirus COVID-19, such as the reproduction number, fatality rate or length of infectiousness period, based on Romanian patients, as well as be able to predict future outcomes.
UPB at SemEval-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts using Transformers and Multi-Task Learning
In this paper, we describe the systems developed by our team for SemEval-2020 Task 9 that aims to cover two well-known code-mixed languages: Hindi-English and Spanish-English.
BART-TL: Weakly-Supervised Topic Label Generation
We propose a novel solution for assigning labels to topic models by using multiple weak labelers.
Neural Approaches for Natural Language Interfaces to Databases: A Survey
A natural language interface to databases (NLIDB) enables users without technical expertise to easily access information from relational databases.
jurBERT: A Romanian BERT Model for Legal Judgement Prediction
Transformer-based models have become the de facto standard in the field of Natural Language Processing (NLP).
Dialect Identification through Adversarial Learning and Knowledge Distillation on Romanian BERT
Dialect identification is a task with applicability in a vast array of domains, ranging from automatic speech recognition to opinion mining.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Exploring the Power of Romanian BERT for Dialect Identification
Dialect identification represents a key aspect for improving a series of tasks, for example, opinion mining, considering that the location of the speaker can greatly influence the attitude towards a subject.
Distilling the Knowledge of Romanian BERTs Using Multiple Teachers
In this work, we introduce three light and fast versions of distilled BERT models for the Romanian language: Distil-BERT-base-ro, Distil-RoBERT-base, and DistilMulti-BERT-base-ro.
GEST: the Graph of Events in Space and Time as a Common Representation between Vision and Language
GEST alows us to measure the similarity between texts and videos in a semantic and fully explainable way, through graph matching.
UPB at IberLEF-2023 AuTexTification: Detection of Machine-Generated Text using Transformer Ensembles
This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, featured as part of IberLEF-2023.
Explaining Vision and Language through Graphs of Events in Space and Time
Artificial Intelligence makes great advances today and starts to bridge the gap between vision and language.
Improving Legal Judgement Prediction in Romanian with Long Text Encoders
In recent years, the entire field of Natural Language Processing (NLP) has enjoyed amazing novel results achieving almost human-like performance on a variety of tasks.
CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues
Recent advancements in instruction-tuning datasets have predominantly focused on specific tasks like mathematical or logical reasoning.
