7,426 research outputs found
Access to recorded interviews: A research agenda
Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed
Exploring Different Dimensions of Attention for Uncertainty Detection
Neural networks with attention have proven effective for many natural
language processing tasks. In this paper, we develop attention mechanisms for
uncertainty detection. In particular, we generalize standardly used attention
mechanisms by introducing external attention and sequence-preserving attention.
These novel architectures differ from standard approaches in that they use
external resources to compute attention weights and preserve sequence
information. We compare them to other configurations along different dimensions
of attention. Our novel architectures set the new state of the art on a
Wikipedia benchmark dataset and perform similar to the state-of-the-art model
on a biomedical benchmark which uses a large set of linguistic features.Comment: accepted at EACL 201
Tracking of enriched dialog states for flexible conversational information access
Dialog state tracking (DST) is a crucial component in a task-oriented dialog
system for conversational information access. A common practice in current
dialog systems is to define the dialog state by a set of slot-value pairs. Such
representation of dialog states and the slot-filling based DST have been widely
employed, but suffer from three drawbacks. (1) The dialog state can contain
only a single value for a slot, and (2) can contain only users' affirmative
preference over the values for a slot. (3) Current task-based dialog systems
mainly focus on the searching task, while the enquiring task is also very
common in practice. The above observations motivate us to enrich current
representation of dialog states and collect a brand new dialog dataset about
movies, based upon which we build a new DST, called enriched DST (EDST), for
flexible accessing movie information. The EDST supports the searching task, the
enquiring task and their mixed task. We show that the new EDST method not only
achieves good results on Iqiyi dataset, but also outperforms other
state-of-the-art DST methods on the traditional dialog datasets, WOZ2.0 and
DSTC2.Comment: 5 pages, 2 figures, accepted by ICASSP201
Noise or music? Investigating the usefulness of normalisation for robust sentiment analysis on social media data
In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvestigated. Sentiment analysis approaches mostly include spell checking or rule-based normalisation as preprocess- ing and rarely investigate its impact on the task performance. We present an optimised sentiment classifier and investigate to what extent its performance can be enhanced by integrating SMT-based normalisation as preprocessing. Experiments on a test set comprising a variety of user-generated content genres revealed that normalisation improves sentiment classification performance on tweets and blog posts, showing the modelâs ability to generalise to other data genres
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