FrameNet CNL: a Knowledge Representation and Information Extraction Language

The paper presents a FrameNet-based information extraction and knowledge representation framework, called FrameNet-CNL. The framework is used on natural language documents and represents the extracted knowledge in a tailor-made Frame-ontology from which unambiguous FrameNet-CNL paraphrase text can be generated automatically in multiple languages. This approach brings together the fields of information extraction and CNL, because a source text can be considered belonging to FrameNet-CNL, if information extraction parser produces the correct knowledge representation as a result. We describe a state-of-the-art information extraction parser used by a national news agency and speculate that FrameNet-CNL eventually could shape the natural language subset used for writing the newswire articles.Comment: CNL-2014 camera-ready version. The final publication is available at link.springer.co

dBaby: Grounded Language Teaching through Games and Efficient Reinforcement Learning

This paper outlines a project proposal to be submitted to EC H2020 call ICT-29-2018. The purpose of the project is to create a digital Baby (dBaby) - an agent perceiving and interacting with the 3D world and communicating with its Teacher via natural language phrases to achieve the goals set by the Teacher. The novelty of the approach is that neither language nor visual capabilities are hard-coded in dBaby - instead, the Teacher defines a language learning Game grounded in the 3D world, and dBaby learns the language as a byproduct of the reinforcement learning from the raw pixels and character strings while maximizing the rewards in the Game. So far such approach successfully has been demonstrated only in the virtual 3D world with pre-programmed Games where it requires millions of episodes to learn a dozen words. Moving to human Teacher and real 3D environment requires an order-of-magnitude improvement to data-efficiency of the reinforcement learning. A novel Episodic Control based pre-training is demonstrated as a promising approach for bootstrapping the data-efficient reinforcement learning

Discrete Denoising Diffusion Approach to Integer Factorization

Integer factorization is a famous computational problem unknown whether being solvable in the polynomial time. With the rise of deep neural networks, it is interesting whether they can facilitate faster factorization. We present an approach to factorization utilizing deep neural networks and discrete denoising diffusion that works by iteratively correcting errors in a partially-correct solution. To this end, we develop a new seq2seq neural network architecture, employ relaxed categorical distribution and adapt the reverse diffusion process to cope better with inaccuracies in the denoising step. The approach is able to find factors for integers of up to 56 bits long. Our analysis indicates that investment in training leads to an exponential decrease of sampling steps required at inference to achieve a given success rate, thus counteracting an exponential run-time increase depending on the bit-length.Comment: International Conference on Artificial Neural Networks ICANN 202

RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation

By addressing both text-to-AMR parsing and AMR-to-text generation, SemEval-2017 Task 9 established AMR as a powerful semantic interlingua. We strengthen the interlingual aspect of AMR by applying the multilingual Grammatical Framework (GF) for AMR-to-text generation. Our current rule-based GF approach completely covered only 12.3% of the test AMRs, therefore we combined it with state-of-the-art JAMR Generator to see if the combination increases or decreases the overall performance. The combined system achieved the automatic BLEU score of 18.82 and the human Trueskill score of 107.2, to be compared to the plain JAMR Generator results. As for AMR parsing, we added NER extensions to our SemEval-2016 general-domain AMR parser to handle the biomedical genre, rich in organic compound names, achieving Smatch F1=54.0%

Character-level neural translation for multilingual media monitoring in the SUMMA project

The paper steps outside the comfort-zone of the traditional NLP tasks like automatic speech recognition (ASR) and machine translation (MT) to addresses two novel problems arising in the automated multilingual news monitoring: segmentation of the TV and radio program ASR transcripts into individual stories, and clustering of the individual stories coming from various sources and languages into storylines. Storyline clustering of stories covering the same events is an essential task for inquisitorial media monitoring. We address these two problems jointly by engaging the low-dimensional semantic representation capabilities of the sequence to sequence neural translation models. To enable joint multi-task learning for multilingual neural translation of morphologically rich languages we replace the attention mechanism with the sliding-window mechanism and operate the sequence to sequence neural translation model on the character-level rather than on the word-level. The story segmentation and storyline clustering problem is tackled by examining the low-dimensional vectors produced as a side-product of the neural translation process. The results of this paper describe a novel approach to the automatic story segmentation and storyline clustering problem.Comment: LREC-2016 submissio

The SUMMA Platform:Scalable Understanding of Multilingual Media

We present the latest version of the SUMMA platform, an open-source software platform for monitoring and interpreting multi-lingual media, from written news published on the internet to live media broadcasts via satellite or internet streaming.This work was conducted within the scope of the Research and Innovation Action SUMMA, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688139

SUMMA: Integrating Multiple NLP Technologies into an Open-source Platform for Multilingual Media Monitoring

The open-source SUMMA Platform is a highly scalable distributed architecture for monitoring a large number of media broadcasts in parallel, with a lag behind actual broadcast time of at most a few minutes. It assembles numerous state-of-the-art NLP technologies into a fully automated media ingestion pipeline that can record live broadcasts, detect and transcribe spoken content, translate from several languages (original text or transcribed speech) into English,1 recognize Named Entities, detect topics, cluster and summarize documents across language barriers, and extract and store factual claims in these news items. This paper describes the intended use cases and discusses the system design decisions that allowed us to integrate state-of-theart NLP modules into an effective workflow with comparatively little effort