Conversation disentanglement aims to separate intermingled messages into detached sessions, which is a fundamental task in understanding multi-party conversations. Existing work on conversation disentanglement relies heavily upon human-annotated data
sets, which is expensive to obtain in practice. In this work, we explore training a conversation disentanglement model without referencing any human annotations. Our method is built upon the deep co-training algorithm, which consists of two neural networks: a message-pair classifier and a session classifier. The former is responsible of retrieving local relations between two messages while the latter categorizes a message to a session by capturing context-aware information. Both the two networks are initialized respectively with pseudo data built from the unannotated corpus. During the deep co-training process, we use the session classifier as a reinforcement learning component to learn a session assigning policy by maximizing the local rewards given by the message-pair classifier. For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence from the disentangled sessions predicted by the session classifier. Experimental results on the large Movie Dialogue Dataset demonstrate that our proposed approach achieves competitive performance compared to previous supervised methods. Further experiments show that the predicted disentangled conversations can promote the performance on the downstream task of multi-party response selection.
Due to its effectiveness and performance, the Transformer translation model has attracted wide attention, most recently in terms of probing-based approaches. Previous work focuses on using or probing source linguistic features in the encoder. To date
, the way word translation evolves in Transformer layers has not yet been investigated. Naively, one might assume that encoder layers capture source information while decoder layers translate. In this work, we show that this is not quite the case: translation already happens progressively in encoder layers and even in the input embeddings. More surprisingly, we find that some of the lower decoder layers do not actually do that much decoding. We show all of this in terms of a probing approach where we project representations of the layer analyzed to the final trained and frozen classifier level of the Transformer decoder to measure word translation accuracy. Our findings motivate and explain a Transformer configuration change: if translation already happens in the encoder layers, perhaps we can increase the number of encoder layers, while decreasing the number of decoder layers, boosting decoding speed, without loss in translation quality? Our experiments show that this is indeed the case: we can increase speed by up to a factor 2.3 with small gains in translation quality, while an 18-4 deep encoder configuration boosts translation quality by +1.42 BLEU (En-De) at a speed-up of 1.4.
It is challenging to design profitable and practical trading strategies, as stock price movements are highly stochastic, and the market is heavily influenced by chaotic data across sources like news and social media. Existing NLP approaches largely t
reat stock prediction as a classification or regression problem and are not optimized to make profitable investment decisions. Further, they do not model the temporal dynamics of large volumes of diversely influential text to which the market responds quickly. Building on these shortcomings, we propose a deep reinforcement learning approach that makes time-aware decisions to trade stocks while optimizing profit using textual data. Our method outperforms state-of-the-art in terms of risk-adjusted returns in trading simulations on two benchmarks: Tweets (English) and financial news (Chinese) pertaining to two major indexes and four global stock markets. Through extensive experiments and studies, we build the case for our method as a tool for quantitative trading.
The process of devolution of power is one of the fundamental
pillars of the establishment of systems of governance, through
peaceful democratic mechanisms which are reflected in the terms
of trade, of free elections based on multi-party.
In order
to achieve the democratic process for power trading by the
Syrian legislature instituted radical reforms in the system of
government manifested in the (2012 constitution and electoral law
No. 5 of 2014 and the Political Parties Law No. 100 of 2011).
This research tried to highlight the role of these reforms by
clarifying the extent of the impact of the electoral system and the
party on the devolution of power process and the role of the new
Syrian political parties in this process.
Transferring of electronic transport records is characterized by activating its
functional equivalent provided by UNCITRAL model law on electronic commerce, but
some issues may appear related to the burden of proof primarly and finding supplementar
y
rules to govern legal institutions developed by the various rules which organize the
electronic transferring steps which was adopted by the Syrian Maritime trade law , which
expanded the explanatory authority of the trial courts and courts of law hearing the cases to
develop the optimum theories of proof by adapting classical institutions of proof provided
by law of evidence to face electronic proof needs and its functional equivalents used with
modern electronic commerce experience . Also the judicial discretion was expanded to
distinguish the elements of legal institutions developed in the electronic transferring from
those in the non-negotiable electronic documents in order to find the legal rules which
govern commitmentsarising from this way of transfer which left for the court the authority
in many situations to evaluatethiscommitmentslimits and effects, so studying this authority
limits and legal background was necessary .
In view of the economic efficiency of container transport we determine reality and prospects for the development of container transport in Syria and issues that impede growth in container handling at the port of Latakia, by examining the number of in
coming and outgoing containers and cargo quantities almhwah, the importance of Syria's geographical location as a crossroads of three continents and study and analysis of syrian exports and how to increase it by increasing the rate of revenue growth and the declining ratio of production requirements and increase the number of vessels that are received by the port, in the development of logistics in Syria.
The aim of this study was to evaluate the effect of handling on the quality and safety
of Sea Bass, Dicentrarchus labrax. The samples were collected from the marine waters of
Tartous, and were traced from catch till arrival to consumers. Sensorial
and microbial
examinations during fish handling were done, and some physical and chemical
characteristics of waters in the fishing area were determined. The results
showed that the sensorial characteristics differed during fish handling and started to
decrease in the marketplace. The results also showed that the number of microorganisms
increased during fish handling and reached the highest level in the marketplace. This
increase was observed particularly in summer. The gills were the most infected part by
handling followed by the scales and then the eyes. The microbial results also showed the
presence of gram positive bacteria such as Staphylococcus Aureus, and gram negative
bacteria such as Escherichia coli, Proteus sp., Salmonella spp., Shigella sp., Pseudomonas
spp.. The market was the most infecting with these microbes.
