Articles

AbstractAutomatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly. Single document summarization (SDS) systems have benefited from advances in neural encoder-decoder model thanks to the availability of large datasets. However, multi-document summarization (MDS) of news articles has been limited …...

AbstractResearch on summarization has mainly been driven by empirical approaches, crafting systems to perform well on standard datasets with the notion of information Importance remaining latent. We argue that establishing theoretical models of Importance will advance our understanding of the task and help to further improve summarization systems. To this …...

AbstractThe recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of why they perform so well, or how they might be improved. In this paper, we seek to better understand how neural extractive summarization systems could benefit …...

In this study, we focus on the issue of noise distortion in speech signals, and develop two novel unsupervised speech enhancement algorithms including temporal lowpass filtering (TLP) and relative-to-maximum masking (RMM). Both of these two algorithms are conducted on the magnitude spectrogram of speech signals. TLP uses a simple moving-average …...

This research aims to develop a parallel sentence extraction method for automatically extracting parallel sentence pairs from bilingual comparable corpora based on cross-lingual word embeddings. Our task is to effectively identify matched sentence pairs from a Chinese- English corpus with the goal of maximizing F1 score. Our method employs pre-trained, …...

The paper proposes a system that compensates most of the noise in a text in natural language caused by technical imperfection of the input device such as keyboard or scanner with optical character recognition, quick typing, or writer incompetence. Correcting the spelling errors in the text improves the performance of …...

In the insurance industry, lots of effort is putting into helping the customer to solve their problems that occurred during and after purchasing cycle and helping telemarketers to practice selling skills. Chat bots and assistant bots are widely used in these business scenarios, but building a bot application from scratch …...

Most of the existing Question Answer Systems focused on searching answers from the Knowledge-Base (KB), and ignore context aware information. Many Question Answer models perform well on public data-sets, but too complicated to be efficient in real world cases. Effectiveness, concurrency and system availability are equally important in industry which …...

This study aims to investigate the occurrences of opposites in Mandarin four-word idioms. We used the Revised Ministry of Education Online Dictionary to search for a total of 12 combinations of opposites in four-word idioms based on 32 opposites. These idioms were analyzed in terms of their dictionary definitions, combinations, …...

Identifying argument components has become an important area of research in argument mining. When argument components are identified, they can not only be used for stance classification but also can provide reasons for determining an article is supporting or opposing about a specific target. Previous research mainly used text classification …...

This paper describes the development and first evaluation of the new Slovak children speech audio corpus for improving the automatic broadcast news subtitling engine developed on the Technical University of Kosice in cooperation with the Slovak Academy of Sciences. The current automatic speech recognition (ASR) systems are reliable for a …...

Human perception on the singing voice differs with the factors of the singing voice and the subjects. On one hand, the background knowledge influences the understanding of voice for each subject. On the other hand, the difference of the voices presented to the subjects also affects the perception. In this …...

AbstractThis paper seeks to model human language by the mathematical framework of quantum physics. With the well-designed mathematical formulations in quantum physics, this framework unifies different linguistic units in a single complex-valued vector space, e.g. words as particles in quantum states and sentences as mixed systems. A complex-valued network is …...

AbstractRecent work has improved our ability to detect linguistic knowledge in word representations. However, current methods for detecting syntactic knowledge do not test whether syntax trees are represented in their entirety. In this work, we propose a structural probe, which evaluates whether syntax trees are embedded in a linear transformation …...

AbstractLemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in low-resource languages. In …...

AbstractIn recent years neural language models (LMs) have set the state-of-the-art performance for several benchmarking datasets. While the reasons for their success and their computational demand are well-documented, a comparison between neural models and more recent developments in n-gram models is neglected. In this paper, we examine the recent progress …...

AbstractFine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains. In a standard fine-tuning scheme, source and target problems are trained using the same architecture. Although capable of adapting to new domains, pre-trained units struggle with learning uncommon target-specific patterns. In this paper, we propose …...

AbstractWhile idiosyncrasies of the Chinese classifier system have been a richly studied topic among linguists (Adams and Conklin, 1973; Erbaugh, 1986; Lakoff, 1986), not much work has been done to quantify them with statistical methods. In this paper, we introduce an information-theoretic approach to measuring idiosyncrasy; we examine how much …...

AbstractThis paper presents three hybrid models that directly combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions. Two of our models get F-scores over the current state-of-the-art using automatic methods on the DementiaBank dataset....

AbstractA number of psycholinguistic studies have factorially manipulated words’ contextual predictabilities and corpus frequencies and shown separable effects of each on measures of human sentence processing, a pattern which has been used to support distinct mechanisms underlying prediction on the one hand and lexical retrieval on the other. This paper …...