Discounting in LTL

In recent years, there is growing need and interest in formalizing and reasoning about the quality of software and hardware systems. As opposed to traditional verification, where one handles the question of whether a system satisfies, or not, a given specification, reasoning about quality addresses the question of \emph{how well} the system satisfies the specification. One direction in this effort is to refine the "eventually" operators of temporal logic to {\em discounting operators}: the satisfaction value of a specification is a value in $[0,1]$, where the longer it takes to fulfill eventuality requirements, the smaller the satisfaction value is. In this paper we introduce an augmentation by discounting of Linear Temporal Logic (LTL), and study it, as well as its combination with propositional quality operators. We show that one can augment LTL with an arbitrary set of discounting functions, while preserving the decidability of the model-checking problem. Further augmenting the logic with unary propositional quality operators preserves decidability, whereas adding an average-operator makes some problems undecidable. We also discuss the complexity of the problem, as well as various extensions

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report. The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn

Domain adaptation : Retraining NMT with translation memories

The topic of this thesis is domain adaptation of an NMT system by retraining it with translation memories. The translation memory used in the experiments is the EMEA corpus that consists of medical texts – mostly package leaflets. The NMT system used in the experiments is OpenNMT because it is completely free and easy to use. The goal of this thesis is to find out how an NMT system can be adapted to a special domain, and if the translation quality improves after domain adaptation. The original plan was to continue training the pretrained model of OpenNMT with EMEA data, but this is not possible. Therefore, it is necessary to train a new baseline model with the same data as the pretrained model was trained with. After this two domain adaptation methods are tested: continuation training with EMEA data and continuation training with unknown terms. In the manual evaluation, it turned out that domain adaptation with unknown terms worsens the translation quality drastically because all sentences are translated as single words. This method is only suitable for translating wordlists because it improved the translation of unknown terms. Domain adaptation with EMEA data, for the other hand, improves the translation quality significantly. The EMEA-retrained system translates long sentences and medical terms much better than the pretrained and the baseline models. Long and complicated terms are still difficult to translate but the EMEA-retrained model makes fewer errors than the other models. The evaluation metrics used for automatic evaluation are BLEU and LeBLEU. BLEU is stricter than LeBLEU. The results are similar as in the manual evaluation: The EMEA-retrained model translates medical texts much better than the other models, and the translation quality of the UNK-retrained model is the worst of all. It can be presumed that an NMT system needs contextual information so that it learns to translate terms and long sentences without transforming the text into a wordlist without sentences. In addition, it seems that long terms are translated in smaller pieces so that the NMT system possibly translates some pieces wrong, which results in that the whole term is wrong