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Found 23 papers, 8 papers with code
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding
Natural language understanding is an important task in modern dialogue systems.
Intent Classification
Intent Classification and Slot Filling
+3
Findings of the LoResMT 2020 Shared Task on Zero-Shot for Low-Resource languages
This paper presents the findings of the LoResMT 2020 Shared Task on zero-shot translation for low resource languages.
Single Example Can Improve Zero-Shot Data Generation
We explore two approaches to the generation of task-oriented utterances: in the zero-shot approach, the model is trained to generate utterances from seen intents and is further used to generate utterances for intents unseen during training.
Multiple Teacher Distillation for Robust and Greener Models
The language models nowadays are in the center of natural language processing progress.
WikiMulti: a Corpus for Cross-Lingual Summarization
Cross-lingual summarization (CLS) is the task to produce a summary in one particular language for a source document in a different language.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models
In the last year, new neural architectures and multilingual pre-trained models have been released for Russian, which led to performance evaluation problems across a range of language understanding tasks.
A Single Example Can Improve Zero-Shot Data Generation
In the zero-shot approach, the model is trained to generate utterances from seen intents and is further used to generate utterances for intents unseen during training.
How not to Lie with a Benchmark: Rearranging NLP Learderboards
Proper model ranking and comparison with a human level is an essential requirement for every benchmark to be a reliable measurement of the model quality.
MOROCCO: Model Resource Comparison Framework
The new generation of pre-trained NLP models push the SOTA to the new limits, but at the cost of computational resources, to the point that their use in real production environments is often prohibitively expensive.
SumTitles: a Summarization Dataset with Low Extractiveness
The existing dialogue summarization corpora are significantly extractive.
RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -- RussianGLUE.
Ranked #1 on
Word Sense Disambiguation
on RUSSE
Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification
Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling.
Humans Keep It One Hundred: an Overview of AI Journey
Artificial General Intelligence (AGI) is showing growing performance in numerous applications - beating human performance in Chess and Go, using knowledge bases and text sources to answer questions (SQuAD) and even pass human examination (Aristo project).
The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews
For the sentence classification task, our model achieves the macro F1 score of 68. 82% gaining 7. 47% over the score of BERT model trained on Russian data.
RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering.
Ranked #2 on
Recommendation Systems
on Million Song Dataset
AspeRa: Aspect-Based Rating Prediction Based on User Reviews
We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items.
Robust to Noise Models in Natural Language Processing Tasks
There are a lot of noise texts surrounding a person in modern life.
The Second Conversational Intelligence Challenge (ConvAI2)
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots.
Self-Attentive Model for Headline Generation
Headline generation is a special type of text summarization task.
AspeRa: Aspect-based Rating Prediction Model
We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users.
Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts
In this work, we consider the medical concept normalization problem, i. e., the problem of mapping a disease mention in free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS).
Robust Word Vectors: Context-Informed Embeddings for Noisy Texts
We suggest a new language-independent architecture of robust word vectors (RoVe).
DeepPavlov: Open-Source Library for Dialogue Systems
It supports modular as well as end-to-end approaches to implementation of conversational agents.
