End-to-end optimization of goal-driven and visually grounded dialogue systems

End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning. Yet, most current approaches cast human-machine dialogue management as a supervised learning problem, aiming at predicting the next utterance of a participant given the full history of the dialogue. This vision is too simplistic to render the intrinsic planning problem inherent to dialogue as well as its grounded nature, making the context of a dialogue larger than the sole history. This is why only chit-chat and question answering tasks have been addressed so far using end-to-end architectures. In this paper, we introduce a Deep Reinforcement Learning method to optimize visually grounded task-oriented dialogues, based on the policy gradient algorithm. This approach is tested on a dataset of 120k dialogues collected through Mechanical Turk and provides encouraging results at solving both the problem of generating natural dialogues and the task of discovering a specific object in a complex picture

What Does Aspect-Oriented Programming Mean for Functional Programmers?

Aspect-Oriented Programming (AOP) aims at modularising crosscutting concerns that show up in software. The success of AOP has been almost viral and nearly all areas in Software Engineering and Programming Languages have become "infected" by the AOP bug in one way or another. Interestingly the functional programming community (and, in particular, the pure functional programming community) seems to be resistant to the pandemic. The goal of this paper is to debate the possible causes of the functional programming community's resistance and to raise awareness and interest by showcasing the benefits that could be gained from having a functional AOP language. At the same time, we identify the main challenges and explore the possible design-space

Transparent Replication Using Metaprogramming in Cyan

Replication can be used to increase the availability of a service by creating many operational copies of its data called replicas. Active replication is a form of replication that has strong consistency semantics, easier to reason about and program. However, creating replicated services using active replication still demands from the programmer the knowledge of subtleties of the replication mechanism. In this paper we show how to use the metaprogramming infrastructure of the Cyan language to shield the application programmer from these details, allowing easier creation of fault-tolerant replicated applications through simple annotations.Comment: 8 page

C to O-O Translation: Beyond the Easy Stuff

Can we reuse some of the huge code-base developed in C to take advantage of modern programming language features such as type safety, object-orientation, and contracts? This paper presents a source-to-source translation of C code into Eiffel, a modern object-oriented programming language, and the supporting tool C2Eif. The translation is completely automatic and supports the entire C language (ANSI, as well as many GNU C Compiler extensions, through CIL) as used in practice, including its usage of native system libraries and inlined assembly code. Our experiments show that C2Eif can handle C applications and libraries of significant size (such as vim and libgsl), as well as challenging benchmarks such as the GCC torture tests. The produced Eiffel code is functionally equivalent to the original C code, and takes advantage of some of Eiffel's object-oriented features to produce safe and easy-to-debug translations