26 research outputs found
xDial-Eval: A Multilingual Open-Domain Dialogue Evaluation Benchmark
Recent advancements in reference-free learned metrics for open-domain
dialogue evaluation have been driven by the progress in pre-trained language
models and the availability of dialogue data with high-quality human
annotations. However, current studies predominantly concentrate on English
dialogues, and the generalization of these metrics to other languages has not
been fully examined. This is largely due to the absence of a multilingual
dialogue evaluation benchmark. To address the issue, we introduce xDial-Eval,
built on top of open-source English dialogue evaluation datasets. xDial-Eval
includes 12 turn-level and 6 dialogue-level English datasets, comprising 14930
annotated turns and 8691 annotated dialogues respectively. The English dialogue
data are extended to nine other languages with commercial machine translation
systems. On xDial-Eval, we conduct comprehensive analyses of previous
BERT-based metrics and the recently-emerged large language models. Lastly, we
establish strong self-supervised and multilingual baselines. In terms of
average Pearson correlations over all datasets and languages, the best baseline
outperforms OpenAI's ChatGPT by absolute improvements of 6.5% and 4.6% at the
turn and dialogue levels respectively, albeit with much fewer parameters. The
data and code are publicly available at https://github.com/e0397123/xDial-Eval.Comment: Accepted to EMNLP-2023 Finding
Overview of Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems at DSTC 11 Track 4
The advent and fast development of neural networks have revolutionized the
research on dialogue systems and subsequently have triggered various challenges
regarding their automatic evaluation. Automatic evaluation of open-domain
dialogue systems as an open challenge has been the center of the attention of
many researchers. Despite the consistent efforts to improve automatic metrics'
correlations with human evaluation, there have been very few attempts to assess
their robustness over multiple domains and dimensions. Also, their focus is
mainly on the English language. All of these challenges prompt the development
of automatic evaluation metrics that are reliable in various domains,
dimensions, and languages. This track in the 11th Dialogue System Technology
Challenge (DSTC11) is part of the ongoing effort to promote robust and
multilingual automatic evaluation metrics. This article describes the datasets
and baselines provided to participants and discusses the submission and result
details of the two proposed subtasks
Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration
Building document-grounded dialogue systems have received growing interest as
documents convey a wealth of human knowledge and commonly exist in enterprises.
Wherein, how to comprehend and retrieve information from documents is a
challenging research problem. Previous work ignores the visual property of
documents and treats them as plain text, resulting in incomplete modality. In
this paper, we propose a Layout-aware document-level Information Extraction
dataset, LIE, to facilitate the study of extracting both structural and
semantic knowledge from visually rich documents (VRDs), so as to generate
accurate responses in dialogue systems. LIE contains 62k annotations of three
extraction tasks from 4,061 pages in product and official documents, becoming
the largest VRD-based information extraction dataset to the best of our
knowledge. We also develop benchmark methods that extend the token-based
language model to consider layout features like humans. Empirical results show
that layout is critical for VRD-based extraction, and system demonstration also
verifies that the extracted knowledge can help locate the answers that users
care about.Comment: Accepted to ACM Multimedia (MM) Industry Track 202
Genome-Wide Association Study Identified a Narrow Chromosome 1 Region Associated with Chicken Growth Traits
Chicken growth traits are important economic traits in broilers. A large number of studies are available on finding genetic factors affecting chicken growth. However, most of these studies identified chromosome regions containing putative quantitative trait loci and finding causal mutations is still a challenge. In this genome-wide association study (GWAS), we identified a narrow 1.5 Mb region (173.5–175 Mb) of chicken (Gallus gallus) chromosome (GGA) 1 to be strongly associated with chicken growth using 47,678 SNPs and 489 F2 chickens. The growth traits included aggregate body weight (BW) at 0–90 d of age measured weekly, biweekly average daily gains (ADG) derived from weekly body weight, and breast muscle weight (BMW), leg muscle weight (LMW) and wing weight (WW) at 90 d of age. Five SNPs in the 1.5 Mb KPNA3-FOXO1A region at GGA1 had the highest significant effects for all growth traits in this study, including a SNP at 8.9 Kb upstream of FOXO1A for BW at 22–48 d and 70 d, a SNP at 1.9 Kb downstream of FOXO1A for WW, a SNP at 20.9 Kb downstream of ENSGALG00000022732 for ADG at 29–42 d, a SNP in INTS6 for BW at 90 d, and a SNP in KPNA3 for BMW and LMW. The 1.5 Mb KPNA3-FOXO1A region contained two microRNA genes that could bind to messenger ribonucleic acid (mRNA) of IGF1, FOXO1A and KPNA3. It was further indicated that the 1.5 Mb GGA1 region had the strongest effects on chicken growth during 22–42 d
A fracture evaluation by acoustic logging technology in oil-based mud: A case from tight sandstone reservoirs in Keshen area of Kuqa Depression, Tarim Basin, NW China
To solve the problem of poor fracture identifying effect on electrical logging in oil-based mud, the application of acoustic logging to the quantitative characterisation of fractures is expanded from three aspects, namely, Stoneley waves, longitudinal and transverse waves and cross dipole acoustic waves, and a fracture logging evaluation model closely related to production capacity is established considering the radial extension characteristics of fractures. The Stoneley reflection coefficient is used to determine fractures locations to help detect fractures during normal micro-resistivity imaging logging. Based on the experiment on the relationship between fracture width and acoustic attenuation coefficient, empirical formulae for calculating fracture width have been established by primary wave and shear wave energy information considering the effect of porosity. The new parameters, including spectrum correlation coefficient and energy difference from cross dipole array acoustic logging data, can be used for fractures evaluation. The more developed the fractures are, the greater the energy difference becomes, and the smaller the spectrum correlation coefficient is, the higher the production is. The fracture effective evaluation parameters can be separated into two components, specified as the degree of fracture vertical opening and radial extension. Combining the conventional logging and array acoustic logging (including cross dipole array acoustic logging), a fracture radial extension evaluation model is presented closely related to productivity. Key words: tight sandstone reservoir, fracture evaluation, acoustic logging, oil-based mud, radial extension, Kuqa, Tarim Basi
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The role of networks in international acquisition premiums
Our work builds on network theory to investigate the role of alliance networks in international acquisition premiums. On the one hand, we postulate that an international acquirer's network centrality in the target country lowers the inclination of offering higher bid premiums associated with its liability of foreignness (i.e., negatively moderates the relation between foreignness and premiums). On the other hand, we provide a perspective that a target firm's local network centrality increases an international acquirer's willingness to pay higher premiums in order to gain access to unique and valuable local knowledge and resources (i.e., positively moderates the relationship between foreignness and premiums). To test our hypotheses, we analyzed a sample of 1693 related acquisition bids made in more than 40 countries between 2008 and 2017. Our findings support our dual perspective on the role of networks and demonstrate that the acquirer's networks and the target's networks have distinct influences on the relationship between foreignness and bid premiums. This study makes contributions to the understanding of the complex dynamics at play in international M & As and emphasizes the importance of distinguishing between the acquirer's and the target's networks in shaping acquisition premiums
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Learning from inbound foreign acquisitions for outbound expansion by emerging market MNEs
Although cross-border acquisitions (CBAs) are prevalent, many such acquisitions fail to complete. This challenge is even more profound for emerging market MNEs (EMNEs). Drawing upon the vicarious learning theory, we argue that EMNEs can learn from inbound foreign acquirers through the latter's demonstration, professional services firms, and employees. This learning mechanism enables EMNEs to better deal with the complexity and uncertainty in various stages of acquiring foreign firms, thus increasing the completion rate of their outbound CBAs. We also suggest that the effectiveness of vicarious learning is further enhanced by the relatedness between inbound and outbound CBAs. Our analysis of 3599 outbound CBAs from 27 emerging economies during 2000-2018 shows that prior inbound CBAs completed in an emerging economy have a positive effect on the completion likelihood of outbound CBAs conducted by EMNEs from this economy. This positive effect becomes even stronger when the percentage of (1) inbound CBAs served by the EMNE's financial advisors, (2) inbound foreign acquirers that are in the same industry as the EMNE, and (3) inbound foreign acquirers that are from the same country as a focal outbound CBA's target country, is larger. These findings offer new insights into the inbound-outbound acquisition links and the internationalization process of EMNEs
Cement bond quality evaluation based on acoustic variable density logging
A new method of cement bond quality evaluation was proposed by combining numerical simulation and calibrated cased hole acoustic logging data. The effects of the cement channel angle and the quality of the second bond interface (the interface of cement with formation) on acoustic variable density logging data were analyzed. Based on the analysis result, a new cement bond evaluation standard was presented after revising the traditional CBL/VDL method. The axisymmetric acoustic field was simulated by real axis integral method, while the non-axisymmetric acoustic field was simulated by 2.5-D finite differential method. After comparing with the calibrated cased hole acoustic logging data, the research has the below results: the numerical simulation result matches with the calibrated well logging data very well and the new method is reliable; the amplitude of the first acoustic arrival in the case hole decreases as the angle of cement channel decreases, and the denser the cement is, the faster the amplitude of cased hole acoustic waveform decays; the lower limit of cement channel angle is around 45 degrees which can be detected by acoustic logging; the formation acoustic waveform is not easy to be detected in time domain, however it is easy to be detected in frequency domain, especially in limestone formation, the first arrival only can be detected when the annulus width of the second bond interface is small. According to the research result of the numerical simulation of cased hole acoustic field and acoustic variable density logging data, new evaluation criteria of cement bound quality were presented. Key words: cement bond quality, acoustic variable density logging, acoustic field in cased well, cement channel angel, interface bond inde