The paper proposes a novel machine learning-based approach to the pathfinding problem on extremely large graphs. This method leverages diffusion distance estimation via a neural network and uses beam search for pathfinding. We demonstrate its efficiency by finding solutions for 4x4x4 and 5x5x5 Rubik's cubes with unprecedentedly short solution lengths, outperforming all available solvers and introducing the first machine learning solver beyond the 3x3x3 case. In particular, it surpasses every single case of the combined best results in the Kaggle Santa 2023 challenge, which involved over 1,000 teams. For the 3x3x3 Rubik's cube, our approach achieves an optimality rate exceeding 98%, matching the performance of task-specific solvers and significantly outperforming prior solutions such as DeepCubeA (60.3%) and EfficientCube (69.6%). Our solution in its current implementation is approximately 25.6 times faster in solving 3x3x3 Rubik's cubes while requiring up to 8.5 times less model training time than the most efficient state-of-the-art competitor. Finally, it is demonstrated that even a single agent trained using a relatively small number of examples can robustly solve a broad range of puzzles represented by Cayley graphs of size up to $10^{145}$, confirming the generality of the proposed method.

Olga Kovaleva, Alexey Romanov, Anna Rogers, Anna Rumshisky. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss’ kappa of 0.58 (3 annotations per post). To diversify the dataset, 6,950 posts were pre-selected with an active learning-style strategy. We report baseline classification results, and we also release the best-performing embeddings trained on 3.2B tokens of Russian VKontakte posts.

This paper addresses the problem of representation learning.Using an autoencoder framework, we propose and evaluate several loss functions that can be used as an alternative to the commonly used cross-entropy reconstruction loss.The proposed loss functions use similarities between words in the embedding space, and can be used to train any neural model for text generation.We show that the introduced loss functions amplify semantic diversity of reconstructed sentences, while preserving the original meaning of the input.We test the derived autoencoder-generated representations on paraphrase detection and language inference tasks and demonstrate performance improvement compared to the traditional cross-entropy loss.

State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs.However, they still lack generalization capabilities in conditions that differ from the ones encountered during training.This is even more challenging in specialized, and knowledge intensive domains, where training data is limited.To address this gap, we introduce MedNLI 1 -a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients.We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g.SNLI) and 2) incorporate domain knowledge from external data and lexical sources (e.g.medical terminologies).Our results demonstrate performance gains using both strategies.

In this paper, we propose to use a set of simple, uniform in architecture LSTMbased models to recover different kinds of temporal relations from text.Using the shortest dependency path between entities as input, the same architecture is implemented to extract intra-sentence, crosssentence, and document creation time relations.A "double-checking" technique reverses entity pairs in classification, boosting the recall of positive cases and reducing misclassifications between opposite classes.An efficient pruning algorithm resolves conflicts globally.Evaluated on QA-TempEval (SemEval2015 Task 5), our proposed technique outperforms state-ofthe-art methods by a large margin.We also conduct intrinsic evaluation and post stateof-the-art results on Timebank-Dense.

In order to determine argument structure in text, one must understand how individual components of the overall argument are linked. This work presents the first neural network-based approach to link extraction in argument mining. Specifically, we propose a novel architecture that applies Pointer Network sequence-to-sequence attention modeling to structural prediction in discourse parsing tasks. We then develop a joint model that extends this architecture to simultaneously address the link extraction task and the classification of argument components. The proposed joint model achieves state-of-the-art results on two separate evaluation corpora, showing far superior performance than the previously proposed corpus-specific and heavily feature-engineered models. Furthermore, our results demonstrate that jointly optimizing for both tasks is crucial for high performance.

This paper demonstrates the effectiveness of a Long Short-Term Memory language model in our initial efforts to generate unconstrained rap lyrics.The goal of this model is to generate lyrics that are similar in style to that of a given rapper, but not identical to existing lyrics: this is the task of ghostwriting.Unlike previous work, which defines explicit templates for lyric generation, our model defines its own rhyme scheme, line length, and verse length.Our experiments show that a Long Short-Term Memory language model produces better "ghostwritten" lyrics than a baseline model.

PRE-EARTHQUAKES (Processing Russian and European EARTH observations for earthQUAKE precursors Studies) EU-FP7 project is devoted to demonstrate - integrating different observational data, comparing and improving different data analysis methods - how it is possible to progressively increase reliability of short term seismic risk assessment. Three main testing area were selected (Italy, Turkey and Sakhalin) in order to concentrate observations and integration efforts starting with a learning phase on selected events in the past devoted to identify the most suitable parameters, observations technologies, data analysis algorithms. For these areas, different ground (80 radon and 29 spring water stations in Turkey region, 2 magneto-telluric in Italy) and satellite (18 different systems) based observations, 11 data analysis methods, for 7 measured parameters, have been compared and integrated. A specific integration platform (PEG, Pre-Earthquakes Geoportal) based on OGC (Open Geospatial Consortium) standards, was developed to operate a products integration, cross-validation and scientific interpretation.