Irina Piontkovskaya


Ask Me Anything in Your Native Language
Nikita Sorokin | Dmitry Abulkhanov | Irina Piontkovskaya | Valentin Malykh
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Cross-lingual question answering is a thriving field in the modern world, helping people to search information on the web more efficiently. One of the important scenarios is to give an answer even there is no answer in the language a person asks a question with. We present a novel approach based on single encoder for query and passage for retrieval from multi-lingual collection, together with cross-lingual generative reader. It achieves a new state of the art in both retrieval and end-to-end tasks on the XOR TyDi dataset outperforming the previous results up to 10% on several languages. We find that our approach can be generalized to more than 20 languages in zero-shot approach and outperform all previous models by 12%.

Acceptability Judgements via Examining the Topology of Attention Maps
Daniil Cherniavskii | Eduard Tulchinskii | Vladislav Mikhailov | Irina Proskurina | Laida Kushnareva | Ekaterina Artemova | Serguei Barannikov | Irina Piontkovskaya | Dmitri Piontkovski | Evgeny Burnaev
Findings of the Association for Computational Linguistics: EMNLP 2022

The role of the attention mechanism in encoding linguistic knowledge has received special interest in NLP. However, the ability of the attention heads to judge the grammatical acceptability of a sentence has been underexplored. This paper approaches the paradigm of acceptability judgments with topological data analysis (TDA), showing that the geometric properties of the attention graph can be efficiently exploited for two standard practices in linguistics: binary judgments and linguistic minimal pairs. Topological features enhance the BERT-based acceptability classifier scores by 8%-24% on CoLA in three languages (English, Italian, and Swedish). By revealing the topological discrepancy between attention maps of minimal pairs, we achieve the human-level performance on the BLiMP benchmark, outperforming nine statistical and Transformer LM baselines. At the same time, TDA provides the foundation for analyzing the linguistic functions of attention heads and interpreting the correspondence between the graph features and grammatical phenomena. We publicly release the code and other materials used in the experiments.

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Template-based Approach to Zero-shot Intent Recognition
Dmitry Lamanov | Pavel Burnyshev | Katya Artemova | Valentin Malykh | Andrey Bout | Irina Piontkovskaya
Proceedings of the 15th International Conference on Natural Language Generation


Single Example Can Improve Zero-Shot Data Generation
Pavel Burnyshev | Valentin Malykh | Andrey Bout | Ekaterina Artemova | Irina Piontkovskaya
Proceedings of the 14th International Conference on Natural Language Generation

Sub-tasks of intent classification, such as robustness to distribution shift, adaptation to specific user groups and personalization, out-of-domain detection, require extensive and flexible datasets for experiments and evaluation. As collecting such datasets is time- and labor-consuming, we propose to use text generation methods to gather datasets. The generator should be trained to generate utterances that belong to the given intent. 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. In the one-shot approach, the model is presented with a single utterance from a test intent. We perform a thorough automatic, and human evaluation of the intrinsic properties of two-generation approaches. The attributes of the generated data are close to original test sets, collected via crowd-sourcing.

Artificial Text Detection via Examining the Topology of Attention Maps
Laida Kushnareva | Daniil Cherniavskii | Vladislav Mikhailov | Ekaterina Artemova | Serguei Barannikov | Alexander Bernstein | Irina Piontkovskaya | Dmitri Piontkovski | Evgeny Burnaev
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The impressive capabilities of recent generative models to create texts that are challenging to distinguish from the human-written ones can be misused for generating fake news, product reviews, and even abusive content. Despite the prominent performance of existing methods for artificial text detection, they still lack interpretability and robustness towards unseen models. To this end, we propose three novel types of interpretable topological features for this task based on Topological Data Analysis (TDA) which is currently understudied in the field of NLP. We empirically show that the features derived from the BERT model outperform count- and neural-based baselines up to 10% on three common datasets, and tend to be the most robust towards unseen GPT-style generation models as opposed to existing methods. The probing analysis of the features reveals their sensitivity to the surface and syntactic properties. The results demonstrate that TDA is a promising line with respect to NLP tasks, specifically the ones that incorporate surface and structural information.

InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding
Pavel Burnyshev | Andrey Bout | Valentin Malykh | Irina Piontkovskaya
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Natural language understanding is an important task in modern dialogue systems. It becomes more important with the rapid extension of the dialogue systems’ functionality. In this work, we present an approach to zero-shot transfer learning for the tasks of intent classification and slot-filling based on pre-trained language models. We use deep contextualized models feeding them with utterances and natural language descriptions of user intents to get text embeddings. These embeddings then used by a small neural network to produce predictions for intent and slot probabilities. This architecture achieves new state-of-the-art results in two zero-shot scenarios. One is a single language new skill adaptation and another one is a cross-lingual adaptation.

Multiple Teacher Distillation for Robust and Greener Models
Artur Ilichev | Nikita Sorokin | Irina Piontkovskaya | Valentin Malykh
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

The language models nowadays are in the center of natural language processing progress. These models are mostly of significant size. There are successful attempts to reduce them, but at least some of these attempts rely on randomness. We propose a novel distillation procedure leveraging on multiple teachers usage which alleviates random seed dependency and makes the models more robust. We show that this procedure applied to TinyBERT and DistilBERT models improves their worst case results up to 2% while keeping almost the same best-case ones. The latter fact keeps true with a constraint on computational time, which is important to lessen the carbon footprint. In addition, we present the results of an application of the proposed procedure to a computer vision model ResNet, which shows that the statement keeps true in this totally different domain.


SumTitles: a Summarization Dataset with Low Extractiveness
Valentin Malykh | Konstantin Chernis | Ekaterina Artemova | Irina Piontkovskaya
Proceedings of the 28th International Conference on Computational Linguistics

The existing dialogue summarization corpora are significantly extractive. We introduce a methodology for dataset extractiveness evaluation and present a new low-extractive corpus of movie dialogues for abstractive text summarization along with baseline evaluation. The corpus contains 153k dialogues and consists of three parts: 1) automatically aligned subtitles, 2) automatically aligned scenes from scripts, and 3) manually aligned scenes from scripts. We also present an alignment algorithm which we use to construct the corpus.