93,880 research outputs found
Dialogue Act Recognition via CRF-Attentive Structured Network
Dialogue Act Recognition (DAR) is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DAR problem ranging from multi-classification to structured
prediction, which suffer from handcrafted feature extensions and attentive
contextual structural dependencies. In this paper, we consider the problem of
DAR from the viewpoint of extending richer Conditional Random Field (CRF)
structural dependencies without abandoning end-to-end training. We incorporate
hierarchical semantic inference with memory mechanism on the utterance
modeling. We then extend structured attention network to the linear-chain
conditional random field layer which takes into account both contextual
utterances and corresponding dialogue acts. The extensive experiments on two
major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder
Dialogue Act (MRDA) datasets show that our method achieves better performance
than other state-of-the-art solutions to the problem. It is a remarkable fact
that our method is nearly close to the human annotator's performance on SWDA
within 2% gap.Comment: 10 pages, 4figure
Towards Knowledge-Based Personalized Product Description Generation in E-commerce
Quality product descriptions are critical for providing competitive customer
experience in an e-commerce platform. An accurate and attractive description
not only helps customers make an informed decision but also improves the
likelihood of purchase. However, crafting a successful product description is
tedious and highly time-consuming. Due to its importance, automating the
product description generation has attracted considerable interests from both
research and industrial communities. Existing methods mainly use templates or
statistical methods, and their performance could be rather limited. In this
paper, we explore a new way to generate the personalized product description by
combining the power of neural networks and knowledge base. Specifically, we
propose a KnOwledge Based pErsonalized (or KOBE) product description generation
model in the context of e-commerce. In KOBE, we extend the encoder-decoder
framework, the Transformer, to a sequence modeling formulation using
self-attention. In order to make the description both informative and
personalized, KOBE considers a variety of important factors during text
generation, including product aspects, user categories, and knowledge base,
etc. Experiments on real-world datasets demonstrate that the proposed method
out-performs the baseline on various metrics. KOBE can achieve an improvement
of 9.7% over state-of-the-arts in terms of BLEU. We also present several case
studies as the anecdotal evidence to further prove the effectiveness of the
proposed approach. The framework has been deployed in Taobao, the largest
online e-commerce platform in China.Comment: KDD 2019 Camera-ready. Website:
https://sites.google.com/view/kobe201
Making the most of the G8+5 Climate Change Process: Accelerating Structural Change and Technology Diffusion on a Global Scale. CEPS Task Force Reports, 5 June 2008
Under the chairmanship of Gunnar Still, Senior Vice President and Head of Environment Division at ThyssenKrupp, CEPS organized a Task Force to explore possible initiatives within the context of the G8+5 dialogue on tackling climate change. This report identifies a number of concrete measures that could reduce greenhouse gas (GHG) emissions, while at the same time stimulating structural change and technology development and diffusion. It calls for supporting action-based approaches, which are essential to achieve the necessary reductions in GHG emissions, inform the post-2012 negotiations and address the most urgent issues such as surging energy demand and the need for clean energy technologies in emerging economies. An action-based approach can be regarded as a way of integrating targets and timetables, as they are agreed, with consistent and comparable policies and measures. With a view to a long-term climate strategy, this report attempts to present a portfolio of actions that can be implemented and accelerated on a global scale – especially in the G8+5 countries and the EU, and could become a basis on which developed and developing countries can cooperate
Semi-automatic semantic enrichment of raw sensor data
One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In recent trials to examine the effects that key moments in movies have on the human body, we fitted fitted with a number of biometric sensor devices and monitored them as they watched a range of dierent movies in groups. The purpose of these experiments was to examine the correlation between humans' highlights in movies as observed from biometric sensors, and highlights in the same movies as identified by our automatic movie analysis techniques. However,the problem with this type of experiment is that both the analysis of the video stream and the sensor data readings are not directly usable
in their raw form because of the sheer volume of low-level data values generated both from the sensors and from the movie analysis. This work describes the semi-automated enrichment of both video analysis and sensor data and the mechanism used to query the data in both centralised
environments, and in a peer-to-peer architecture when the number of sensor devices grows to large numbers. We present and validate a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor management
Modelling Users, Intentions, and Structure in Spoken Dialog
We outline how utterances in dialogs can be interpreted using a partial first
order logic. We exploit the capability of this logic to talk about the truth
status of formulae to define a notion of coherence between utterances and
explain how this coherence relation can serve for the construction of AND/OR
trees that represent the segmentation of the dialog. In a BDI model we
formalize basic assumptions about dialog and cooperative behaviour of
participants. These assumptions provide a basis for inferring speech acts from
coherence relations between utterances and attitudes of dialog participants.
Speech acts prove to be useful for determining dialog segments defined on the
notion of completing expectations of dialog participants. Finally, we sketch
how explicit segmentation signalled by cue phrases and performatives is covered
by our dialog model.Comment: 17 page
- …