Riesgos de interpretación errónea en la evaluación de la Supervisión Distante para la Extracción de Relaciones

Distant Supervision is frequently used for addressing Relation Extraction. The evaluation of Distant Supervision in Relation Extraction has been attempted through Precision-Recall curves and/or calculation of Precision at N elements. However, such evaluation is challenging because the labeling of the instances results from an automatic process that can introduce noise into the labels. Consequently, the labels are not necessarily correct, affecting the learning process and the interpretation of the evaluation results. Therefore, this research aims to show that the performance of the methods measured with the mentioned evaluation strategies varies significantly if the correct labels are used during the evaluation. Besides, based on the preceding, the current interpretation of the results of these measures is questioned. To this end, we manually labeled a subset of a well-known data set and evaluated the performance of 6 traditional Distant Supervision approaches. We demonstrate quantitative differences in the evaluation scores when considering manually versus automatically labeled subsets. Consequently, the ranking of performance among distant supervision methods is different with both labeled.La Supervisión Distante se utiliza con frecuencia para abordar la extracción de relaciones. La evaluación de la Supervisión Distante en la Extracción de Relaciones se ha realizado mediante curvas de Precisión-Cobertura y/o el cálculo de la Precisión en N elementos. Sin embargo, dicha evaluación es un desafío porque el etiquetado de las instancias es el resultado de un proceso automático. En consecuencia, las etiquetas no son necesariamente correctas, afectando no solo el proceso de aprendizaje sino también la interpretación de los resultados de la evaluación. El objetivo de esta investigación es mostrar que el desempeño de los métodos medido con las estrategias de evaluación mencionadas varía de manera significativa si se utilizan las etiquetas correctas durante la evaluación. Además, basado en lo anterior, se cuestiona la interpretación actual de los resultados de estas medidas. Con este fin, etiquetamos manualmente un subconjunto de un conjunto de datos y evaluamos el desempeño de 6 enfoques tradicionales de Supervisión Distante. Demostramos diferencias cuantitativas en los puntajes de evaluación al considerar subconjuntos etiquetados manualmente versus automáticamente. En consecuencia, el orden de desempeño entre los métodos de Supervisión Distante es diferente con ambos etiquetados.The present work was supported by CONACyT/México (scholarship 937210 and grant CB-2015-01-257383). Additionally, the authors thank CONACYT for the computer resources provided through the INAOE Supercomputing Laboratory’s Deep Learning Platform for Language Technologies

Query Resolution for Conversational Search with Limited Supervision

In this work we focus on multi-turn passage retrieval as a crucial component of conversational search. One of the key challenges in multi-turn passage retrieval comes from the fact that the current turn query is often underspecified due to zero anaphora, topic change, or topic return. Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution. In this paper, we model the query resolution task as a binary term classification problem: for each term appearing in the previous turns of the conversation decide whether to add it to the current turn query or not. We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We propose a distant supervision method to automatically generate training data by using query-passage relevance labels. Such labels are often readily available in a collection either as human annotations or inferred from user interactions. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrieval architecture and demonstrate its effectiveness on the TREC CAsT dataset.Comment: SIGIR 2020 full conference pape