Nikolai Ilinykh


2023

pdf bib
Context matters: evaluation of target and context features on variation of object naming
Nikolai Ilinykh | Simon Dobnik
Proceedings of the 1st Workshop on Linguistic Insights from and for Multimodal Language Processing

pdf bib
Describe Me an Auklet: Generating Grounded Perceptual Category Descriptions
Bill Noble | Nikolai Ilinykh
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Human speakers can generate descriptions of perceptual concepts, abstracted from the instance-level. Moreover, such descriptions can be used by other speakers to learn provisional representations of those concepts. Learning and using abstract perceptual concepts is under-investigated in the language-and-vision field. The problem is also highly relevant to the field of representation learning in multi-modal NLP. In this paper, we introduce a framework for testing category-level perceptual grounding in multi-modal language models. In particular, we train separate neural networks to **generate** and **interpret** descriptions of visual categories. We measure the *communicative success* of the two models with the zero-shot classification performance of the interpretation model, which we argue is an indicator of perceptual grounding. Using this framework, we compare the performance of *prototype*- and *exemplar*-based representations. Finally, we show that communicative success exposes performance issues in the generation model, not captured by traditional intrinsic NLG evaluation metrics, and argue that these issues stem from a failure to properly ground language in vision at the category level.

pdf bib
Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)
Nikolai Ilinykh | Felix Morger | Dana Dannélls | Simon Dobnik | Beáta Megyesi | Joakim Nivre
Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)

pdf bib
Vector Norms as an Approximation of Syntactic Complexity
Adam Ek | Nikolai Ilinykh
Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)

Internal representations in transformer models can encode useful linguistic knowledge about syntax. Such knowledge could help optimise the data annotation process. However, identifying and extracting such representations from big language models is challenging. In this paper we evaluate two multilingual transformers for the presence of knowledge about the syntactic complexity of sentences and examine different vector norms. We provide a fine-grained evaluation of different norms in different layers and for different languages. Our results suggest that no single part in the models would be the primary source for the knowledge of syntactic complexity. But some norms show a higher degree of sensitivity to syntactic complexity, depending on the language and model used.

pdf bib
The VDG Challenge: Response Generation and Evaluation in Collaborative Visual Dialogue
Nikolai Ilinykh | Simon Dobnik
Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges

We propose the VDG Challenge: a shared task that addresses and benchmarks the task of utterance generation in collaborative visual dialogue. The task features two challenging datasets, an evaluation protocol and a tentative schedule. Our shared task will allow researchers to unravel problems of modelling multi-modal interaction and fit of the existing approaches in the NLP and NLG communities.

pdf bib
Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD)
Ellen Breitholtz | Shalom Lappin | Sharid Loaiciga | Nikolai Ilinykh | Simon Dobnik
Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD)

pdf bib
Are Language-and-Vision Transformers Sensitive to Discourse? A Case Study of ViLBERT
Ekaterina Voloshina | Nikolai Ilinykh | Simon Dobnik
Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)

Language-and-vision models have shown good performance in tasks such as image-caption matching and caption generation. However, it is challenging for such models to generate pragmatically correct captions, which adequately reflect what is happening in one image or several images. It is crucial to evaluate this behaviour to understand underlying reasons behind it. Here we explore to what extent contextual language-and-vision models are sensitive to different discourse, both textual and visual. In particular, we employ one of the multi-modal transformers (ViLBERT) and test if it can match descriptions and images, differentiating them from distractors of different degree of similarity that are sampled from different visual and textual contexts. We place our evaluation in the multi-sentence and multi-image setup, where images and sentences are expected to form a single narrative structure. We show that the model can distinguish different situations but it is not sensitive to differences within one narrative structure. We also show that performance depends on the task itself, for example, what modality remains unchanged in non-matching pairs or how similar non-matching pairs are to original pairs.

2022

pdf bib
Look and Answer the Question: On the Role of Vision in Embodied Question Answering
Nikolai Ilinykh | Yasmeen Emampoor | Simon Dobnik
Proceedings of the 15th International Conference on Natural Language Generation

pdf bib
Do Decoding Algorithms Capture Discourse Structure in Multi-Modal Tasks? A Case Study of Image Paragraph Generation
Nikolai Ilinykh | Simon Dobnik
Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

This paper describes insights into how different inference algorithms structure discourse in image paragraphs. We train a multi-modal transformer and compare 11 variations of decoding algorithms. We propose to evaluate image paragraphs not only with standard automatic metrics, but also with a more extensive, “under the hood” analysis of the discourse formed by sentences. Our results show that while decoding algorithms can be unfaithful to the reference texts, they still generate grounded descriptions, but they also lack understanding of the discourse structure and differ from humans in terms of attentional structure over images.

pdf bib
Attention as Grounding: Exploring Textual and Cross-Modal Attention on Entities and Relations in Language-and-Vision Transformer
Nikolai Ilinykh | Simon Dobnik
Findings of the Association for Computational Linguistics: ACL 2022

We explore how a multi-modal transformer trained for generation of longer image descriptions learns syntactic and semantic representations about entities and relations grounded in objects at the level of masked self-attention (text generation) and cross-modal attention (information fusion). We observe that cross-attention learns the visual grounding of noun phrases into objects and high-level semantic information about spatial relations, while text-to-text attention captures low-level syntactic knowledge between words. This concludes that language models in a multi-modal task learn different semantic information about objects and relations cross-modally and uni-modally (text-only). Our code is available here: https://github.com/GU-CLASP/attention-as-grounding.

pdf bib
In Search of Meaning and Its Representations for Computational Linguistics
Simon Dobnik | Robin Cooper | Adam Ek | Bill Noble | Staffan Larsson | Nikolai Ilinykh | Vladislav Maraev | Vidya Somashekarappa
Proceedings of the 2022 CLASP Conference on (Dis)embodiment

In this paper we examine different meaning representations that are commonly used in different natural language applications today and discuss their limits, both in terms of the aspects of the natural language meaning they are modelling and in terms of the aspects of the application for which they are used.

pdf bib
Examining the Effects of Language-and-Vision Data Augmentation for Generation of Descriptions of Human Faces
Nikolai Ilinykh | Rafal Černiavski | Eva Elžbieta Sventickaitė | Viktorija Buzaitė | Simon Dobnik
Proceedings of the 2nd Workshop on People in Vision, Language, and the Mind

We investigate how different augmentation techniques on both textual and visual representations affect the performance of the face description generation model. Specifically, we provide the model with either original images, sketches of faces, facial composites or distorted images. In addition, on the language side, we experiment with different methods to augment the original dataset with paraphrased captions, which are semantically equivalent to the original ones, but differ in terms of their form. We also examine if augmenting the dataset with descriptions from a different domain (e.g., image captions of real-world images) has an effect on the performance of the models. We train models on different combinations of visual and linguistic features and perform both (i) automatic evaluation of generated captions and (ii) examination of how useful different visual features are for the task of facial feature classification. Our results show that although original images encode the best possible representation for the task, the model trained on sketches can still perform relatively well. We also observe that augmenting the dataset with descriptions from a different domain can boost performance of the model. We conclude that face description generation systems are more susceptible to language rather than vision data augmentation. Overall, we demonstrate that face caption generation models display a strong imbalance in the utilisation of language and vision modalities, indicating a lack of proper information fusion. We also describe ethical implications of our study and argue that future work on human face description generation should create better, more representative datasets.

2021

pdf bib
How Vision Affects Language: Comparing Masked Self-Attention in Uni-Modal and Multi-Modal Transformer
Nikolai Ilinykh | Simon Dobnik
Proceedings of the 1st Workshop on Multimodal Semantic Representations (MMSR)

The problem of interpretation of knowledge learned by multi-head self-attention in transformers has been one of the central questions in NLP. However, a lot of work mainly focused on models trained for uni-modal tasks, e.g. machine translation. In this paper, we examine masked self-attention in a multi-modal transformer trained for the task of image captioning. In particular, we test whether the multi-modality of the task objective affects the learned attention patterns. Our visualisations of masked self-attention demonstrate that (i) it can learn general linguistic knowledge of the textual input, and (ii) its attention patterns incorporate artefacts from visual modality even though it has never accessed it directly. We compare our transformer’s attention patterns with masked attention in distilgpt-2 tested for uni-modal text generation of image captions. Based on the maps of extracted attention weights, we argue that masked self-attention in image captioning transformer seems to be enhanced with semantic knowledge from images, exemplifying joint language-and-vision information in its attention patterns.

2020

pdf bib
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
Thiago Castro Ferreira | Claire Gardent | Nikolai Ilinykh | Chris van der Lee | Simon Mille | Diego Moussallem | Anastasia Shimorina
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

pdf bib
A General Benchmarking Framework for Text Generation
Diego Moussallem | Paramjot Kaur | Thiago Ferreira | Chris van der Lee | Anastasia Shimorina | Felix Conrads | Michael Röder | René Speck | Claire Gardent | Simon Mille | Nikolai Ilinykh | Axel-Cyrille Ngonga Ngomo
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

The RDF-to-text task has recently gained substantial attention due to the continuous growth of RDF knowledge graphs in number and size. Recent studies have focused on systematically comparing RDF-to-text approaches on benchmarking datasets such as WebNLG. Although some evaluation tools have already been proposed for text generation, none of the existing solutions abides by the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles and involves RDF data for the knowledge extraction task. In this paper, we present BENG, a FAIR benchmarking platform for Natural Language Generation (NLG) and Knowledge Extraction systems with focus on RDF data. BENG builds upon the successful benchmarking platform GERBIL, is opensource and is publicly available along with the data it contains.

pdf bib
The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020)
Thiago Castro Ferreira | Claire Gardent | Nikolai Ilinykh | Chris van der Lee | Simon Mille | Diego Moussallem | Anastasia Shimorina
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

WebNLG+ offers two challenges: (i) mapping sets of RDF triples to English or Russian text (generation) and (ii) converting English or Russian text to sets of RDF triples (semantic parsing). Compared to the eponymous WebNLG challenge, WebNLG+ provides an extended dataset that enable the training, evaluation, and comparison of microplanners and semantic parsers. In this paper, we present the results of the generation and semantic parsing task for both English and Russian and provide a brief description of the participating systems.

pdf bib
When an Image Tells a Story: The Role of Visual and Semantic Information for Generating Paragraph Descriptions
Nikolai Ilinykh | Simon Dobnik
Proceedings of the 13th International Conference on Natural Language Generation

Generating multi-sentence image descriptions is a challenging task, which requires a good model to produce coherent and accurate paragraphs, describing salient objects in the image. We argue that multiple sources of information are beneficial when describing visual scenes with long sequences. These include (i) perceptual information and (ii) semantic (language) information about how to describe what is in the image. We also compare the effects of using two different pooling mechanisms on either a single modality or their combination. We demonstrate that the model which utilises both visual and language inputs can be used to generate accurate and diverse paragraphs when combined with a particular pooling mechanism. The results of our automatic and human evaluation show that learning to embed semantic information along with visual stimuli into the paragraph generation model is not trivial, raising a variety of proposals for future experiments.

2019

pdf bib
Tell Me More: A Dataset of Visual Scene Description Sequences
Nikolai Ilinykh | Sina Zarrieß | David Schlangen
Proceedings of the 12th International Conference on Natural Language Generation

We present a dataset consisting of what we call image description sequences, which are multi-sentence descriptions of the contents of an image. These descriptions were collected in a pseudo-interactive setting, where the describer was told to describe the given image to a listener who needs to identify the image within a set of images, and who successively asks for more information. As we show, this setup produced nicely structured data that, we think, will be useful for learning models capable of planning and realising such description discourses.

2018

pdf bib
The Task Matters: Comparing Image Captioning and Task-Based Dialogical Image Description
Nikolai Ilinykh | Sina Zarrieß | David Schlangen
Proceedings of the 11th International Conference on Natural Language Generation

Image captioning models are typically trained on data that is collected from people who are asked to describe an image, without being given any further task context. As we argue here, this context independence is likely to cause problems for transferring to task settings in which image description is bound by task demands. We demonstrate that careful design of data collection is required to obtain image descriptions which are contextually bounded to a particular meta-level task. As a task, we use MeetUp!, a text-based communication game where two players have the goal of finding each other in a visual environment. To reach this goal, the players need to describe images representing their current location. We analyse a dataset from this domain and show that the nature of image descriptions found in MeetUp! is diverse, dynamic and rich with phenomena that are not present in descriptions obtained through a simple image captioning task, which we ran for comparison.