DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues

Interpersonal language style shifting in dialogues is an interesting and almost instinctive ability of human. Understanding interpersonal relationship from language content is also a crucial step toward further understanding dialogues. Previous work mainly focuses on relation extraction between named entities in texts. In this paper, we propose the task of relation classification of interlocutors based on their dialogues. We crawled movie scripts from IMSDb, and annotated the relation labels for each session according to 13 pre-defined relationships. The annotated dataset DDRel consists of 6300 dyadic dialogue sessions between 694 pair of speakers with 53,126 utterances in total. We also construct session-level and pair-level relation classification tasks with widely-accepted baselines. The experimental results show that this task is challenging for existing models and the dataset will be useful for future research.Comment: This paper has been accepted by AAAI202

Bank Credit Strategy Model Based on AHP-Fuzzy Comprehensive Evaluation

Credit risk control and credit strategy formulation of medium and micro enterprises have always been important strategic issues faced by commercial banks. Banks usually make corporate loan policies based on the credit degree, the information of trading bills and the relationship of supply-demand chain of the enterprise. In this paper, we established the AHP-Fuzzy comprehensive evaluation model for quantifying enterprise credit risk. Based on the relevant data of 123 enterprises with credit records, the credit strategy is formulated according to the three indicators of enterprise strength, enterprise reputation and stability of supply-demand relationship. This paper also combines the credit reputation, credit risk and supply and demand stability rating in order to establish the bank credit strategic planning model to decide whether to lend or not and the lending order. The conclusion shows that, under the condition of constant total loan amount, the enterprises with the highest credit rating should be given priority. Then, combined with the change of customer turnover rate with interest rate, we take the bank's maximize expected income as objective to calculate the optimal loan interest rate of different customer groups

Soil respiration at different time scales from 2000 to 2018 in forest ecosystems across China

The related studies on soil respiration (Rs) are increasing year by year in China, amounts of Rs data were published, especially in the form of monthly dynamics figures. Here, we compiled a comprehensive and uniform Rs database in China's forests from 568 literatures published up to 2018, including Rs and the concurrently measured soil temperature (N=8317), mean monthly Rs (N=5003), and annual Rs (N=634). Besides the Rs data directly given in the original papers, the monthly patterns of Rs and the concurrently measured soil temperature at 5 cm and/or 10 cm depth in the figures were digitized. These Rs data derived from the undisturbed forest ecosystems. The common measurement methods were selected, i.e. infrared gas analyzers (model Li-6400, Li-8100, Li-8150 (LI-COR Inc., Lincoln, Nebraska, USA)) and gas chromatography. Meanwhile, the associated information was recorded, e.g. geographical location (province, study site, latitude, longitude and elevation), climate factors (mean annual temperature and mean annual precipitation), stand description (forest type, origin, age, density, mean tree height and diameter at breast height), measurement regime (method, time, frequency, collar area, height and numbers). We hope the database will be used by the science community to provide a better understanding of carbon cycle in China's forests and reduce the uncertainty in evaluating of carbon budget at the large scale

Quantifying the interannual litterfall variations in China's forest ecosystems

Aims Litterfall is a key parameter in forest biogeochemical cycle and fire risk prediction. However, considerable uncertainty remains regarding the litterfall variations with forest ages. Quantifying the interannual variation of forest litterfall is crucial for reducing uncertainties in large-scale litterfall prediction. Methods Based on the available dataset (N = 318) with continuous multi-year (>= 2 years) measurements of litterfall in Chinese planted and secondary forests, coefficient of variation (CV), variation percent (V-P), and the ratio of next-year litterfall to current-year litterfall were used as the indexes to quantify the interannual variability in litterfall. Important Findings The interannual variations of litterfall showed a declining trend with increasing age from 1 to 90 years. The litterfall variations were the largest in 1-10 years (mean CV = 23.51% and mean V-P = -28.59% to 20.89%), which were mainly from tree growth (mean ratio of next-year to current-year= 1.20). In 11-40 years, the interannual variations of litterfall gradually decreased but still varied widely, mean CV was similar to 18% and mean V-P ranged from -17.69% to 21.19%. In 41-90 years, the interannual variations minimized to 8.98% in mean CV and similar to 8% in mean V-P. As a result, forest litterfall remained relatively low and constant when stand age was larger than 40 years. This result was different from the previous assumptions that forest litterfall reached relatively stable when stand age was larger than 30, 20 or even 15 years. Our findings can improve the knowledge about forest litter ecology and provide the groundwork for carbon budget and biogeochemical qcle models at a large scale

Detecting Target Objects by Natural Language Instructions Using an RGB-D Camera

Controlling robots by natural language (NL) is increasingly attracting attention for its versatility, convenience and no need of extensive training for users. Grounding is a crucial challenge of this problem to enable robots to understand NL instructions from humans. This paper mainly explores the object grounding problem and concretely studies how to detect target objects by the NL instructions using an RGB-D camera in robotic manipulation applications. In particular, a simple yet robust vision algorithm is applied to segment objects of interest. With the metric information of all segmented objects, the object attributes and relations between objects are further extracted. The NL instructions that incorporate multiple cues for object specifications are parsed into domain-specific annotations. The annotations from NL and extracted information from the RGB-D camera are matched in a computational state estimation framework to search all possible object grounding states. The final grounding is accomplished by selecting the states which have the maximum probabilities. An RGB-D scene dataset associated with different groups of NL instructions based on different cognition levels of the robot are collected. Quantitative evaluations on the dataset illustrate the advantages of the proposed method. The experiments of NL controlled object manipulation and NL-based task programming using a mobile manipulator show its effectiveness and practicability in robotic applications