Within the tensor product $K \mathop{\otimes_{\cal R}} C_2'$ of any ${}^*$-continuous Kleene algebra $K$ with the polycyclic ${}^*$-continuous Kleene algebra $C_2'$ over two bracket pairs there is a copy of the fixed-point closure of $K$: the centralizer of $C_2'$ in $K \mathop{\otimes_{\cal R}} C_2'$. Using an automata-theoretic representation of elements of $K\mathop{\otimes_{\cal R}} C_2'$ à la Kleene, with the aid of normal form theorems that restrict the occurrences of brackets on paths through the automata, we develop a foundation for a calculus of context-free expressions without variable binders. We also give some results on the bra-ket ${}^*$-continuous Kleene algebra $C_2$, motivate the ``completeness equation'' that distinguishes $C_2$ from $C_2'$, and show that $C_2'$ already validates a relativized form of this equation. final version. 42 pages, 4 figures. References sorted alphabetically

We provide a survey and empirical comparison of the state-of-the-art in neural selective classification for NLP tasks.We also provide a methodological blueprint, including a novel metric called refinement that provides a calibrated evaluation of confidence functions for selective prediction.Finally, we supply documented, open-source code to support the future development of selective prediction techniques.

A number of papers have recently argued in favor of using artificially generated languages to investigate the inductive biases of linguistic models, or to develop models for low-resource languages with underrepresented typologies.But the promise of artificial languages comes with a caveat: if these artificial languages are not sufficiently reflective of natural language, then using them as a proxy may lead to inaccurate conclusions.In this paper, we take a step towards increasing the realism of artificial language by introducing a variant of indexed grammars that draw their weights from hierarchical Pitman-Yor processes.We show that this framework generates languages that emulate the statistics of natural language corpora better than the current approach of directly formulating weighted context-free grammars.

We revisit domain adaptation for parsers in the neural era.First we show that recent advances in word representations greatly diminish the need for domain adaptation when the target domain is syntactically similar to the source domain.As evidence, we train a parser on the Wall Street Journal alone that achieves over 90% F 1 on the Brown corpus.For more syntactically distant domains, we provide a simple way to adapt a parser using only dozens of partial annotations.For instance, we increase the percentage of error-free geometry-domain parses in a held-out set from 45% to 73% using approximately five dozen training examples.In the process, we demonstrate a new state-of-the-art single model result on the Wall Street Journal test set of 94.3%.This is an absolute increase of 1.7% over the previous state-of-the-art of 92.6%.

We propose Odd-Man-Out, a novel task which aims to test different properties of word representations.An Odd-Man-Out puzzle is composed of 5 (or more) words, and requires the system to choose the one which does not belong with the others.We show that this simple setup is capable of teasing out various properties of different popular lexical resources (like WordNet and pre-trained word embeddings), while being intuitive enough to annotate on a large scale.In addition, we propose a novel technique for training multi-prototype word representations, based on unsupervised clustering of ELMo embeddings, and show that it surpasses all other representations on all Odd-Man-Out collections.

We present an approach for answering questions that span multiple sentences and exhibit sophisticated cross-sentence anaphoric phenomena, evaluating on a rich source of such questions -the math portion of the Scholastic Aptitude Test (SAT).By using a tree transducer cascade as its basic architecture, our system (called EU-CLID) propagates uncertainty from multiple sources (e.g.coreference resolution or verb interpretation) until it can be confidently resolved.Experiments show the first-ever results (43% recall and 91% precision) on SAT algebra word problems.We also apply EUCLID to the public Dolphin algebra question set, and improve the state-of-the-art F 1 -score from 73.9% to 77.0%.

We present a simple preordering approach for machine translation based on a featurerich logistic regression model to predict whether two children of the same node in the source-side parse tree should be swapped or not.Given the pair-wise children regression scores we conduct an efficient depth-first branch-and-bound search through the space of possible children permutations, avoiding using a cascade of classifiers or limiting the list of possible ordering outcomes.We report experiments in translating English to Japanese and Korean, demonstrating superior performance as (a) the number of crossing links drops by more than 10% absolute with respect to other state-of-the-art preordering approaches, (b) BLEU scores improve on 2.2 points over the baseline with lexicalised reordering model, and (c) decoding can be carried out 80 times faster.

Extended top-down tree transducers (transducteurs généralisés descendants; see [A. Arnold and M. Dauchet, Bi-transductions de forêts, in Proceedings of the 3rd International Colloquium on Automata, Languages and Programming, Edinburgh University Press, Edinburgh, 1976, pp. 74–86]) received renewed interest in the field of natural language processing. Here those transducers are extensively and systematically studied. Their main properties are identified and their relation to classical top-down tree transducers is exactly characterized. The obtained properties completely explain the Hasse diagram of the induced classes of tree transformations. In addition, it is shown that most interesting classes of transformations computed by extended top-down tree transducers are not closed under composition.

Structural causal models offer a popular framework for exploring causal concepts. However, due to their limited expressiveness, structural models have difficulties coping with such concepts as actual (event-to-event) causation. In this article, we propose a new type of causal model, based on embedding structural considerations in the language of situation calculus. By using situation calculus as a basic language, we leverage its power to express complex, dynamically changing situations and, by relying on structural considerations, we can formulate an effective theory of counterfactuals within the situation-calculus.

There is a lot of talk today about the convergence of computing and communications, and the resulting transformation of the PC, television and mobile phone. Silicon technology is the engine driving this convergence, and silicon platforms will play a key role in shaping these products.STMicroelectronics is a major global supplier of SoC ASICs for mobile and wired communications, mass storage, digital consumer electronics and automotive entertainment systems. Like other companies, ST is developing silicon platforms for convergence products to offer versatility, scalability and faster time-to-market. This signals a move from the custom ASIC model to a system solution model based on platforms and complimentary chips and software from ST and its partners.The general platform concept is nothing new. A classic platform example is the PC with an x86 instruction-set- architecture, standardized buses and fully specified I/O devices. The basic idea of a platform is to avoid designing chips and software from scratch, and to amortize platform investment across present and future designs.What is interesting is how companies are employing platform-strategies in new application domains at the levels of IP building blocks, complex chip design and system software. This trend is partly motivated by a need to secure strategic value chain position in rapidly evolving industries. Some of these platform strategies are unproven or in conflict with each other.This presentation introduces the mobile application processor platform called Nomadik, and explains how it provides a roadmap for ST's vision of the mobile future. It looks at Nomadik from a SoC architecture view, a software view and an interface view, and contains information about ST strategies for hardware abstraction and interfacing external peripherals and software.