Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints

Text generation from a knowledge base aims to translate knowledge triples to natural language descriptions. Most existing methods ignore the faithfulness between a generated text description and the original table, leading to generated information that goes beyond the content of the table. In this paper, for the first time, we propose a novel Transformer-based generation framework to achieve the goal. The core techniques in our method to enforce faithfulness include a new table-text optimal-transport matching loss and a table-text embedding similarity loss based on the Transformer model. Furthermore, to evaluate faithfulness, we propose a new automatic metric specialized to the table-to-text generation problem. We also provide detailed analysis on each component of our model in our experiments. Automatic and human evaluations show that our framework can significantly outperform state-of-the-art by a large margin.Comment: Accepted at ACL202

SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data

The problem of urban event ranking aims at predicting the top-k most risky locations of future events such as traffic accidents and crimes. This problem is of fundamental importance to public safety and urban administration especially when limited resources are available. The problem is, however, challenging due to complex and dynamic spatio-temporal correlations between locations, uneven distribution of urban events in space, and the difficulty to correctly rank nearby locations with similar features. Prior works on event forecasting mostly aim at accurately predicting the actual risk score or counts of events for all the locations. Rankings obtained as such usually have low quality due to prediction errors. Learning-to-rank methods directly optimize measures such as Normalized Discounted Cumulative Gain (NDCG), but cannot handle the spatiotemporal autocorrelation existing among locations. In this paper, we bridge the gap by proposing a novel spatial event ranking approach named SpatialRank. SpatialRank features adaptive graph convolution layers that dynamically learn the spatiotemporal dependencies across locations from data. In addition, the model optimizes through surrogates a hybrid NDCG loss with a spatial component to better rank neighboring spatial locations. We design an importance-sampling with a spatial filtering algorithm to effectively evaluate the loss during training. Comprehensive experiments on three real-world datasets demonstrate that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents and outperform state-of-art methods in terms of NDCG by up to 12.7%.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Operation and results of a vegetable market information and consultation system in Vietnam

This report presents the operation and results of a vegetable market information and consultation system (MICS), set up between 2002 and 2005 in Hanoi, to address marketing problems faced by vegetable farmers. A MICS is a market information system (MIS) combined with debates organised among farmers, traders, and development agents to reach common visions and strategies on marketing. Information collected in the first years relating to indicators of origin and supply deficits was disseminated to farmers and extension agents through newsletters and consultation meetings. The process then focused on making daily prices available, as requested by the farmers. The system was based on a network of contact traders and dissemination was by television. The workshops made it possible to reach a consensus for market opportunities arising from periods of supply deficit for some vegetables and how to take advantage of this situation, especially for tomatoes and cabbage imported from China during the rainy season, which presented some quality differences compared to the local products. With regards to price information, the majority of farmers and traders, surveyed by a quick-impact appraisal, stated they had access through television on a regular basis and that they used price information mostly to bargain with traders. Back up for a permanent ''safe'' vegetable producer and trader association was one of the outputs of the MICS. Our experience shows that price dissemination was easier to sustain (with the involvement of the public sector) than the consultation workshops, generally due to low local capacity in terms of meeting facilitation and the present weakness of extension services and sector organisations. (Résumé d'auteur