4 research outputs found
Table Search Using a Deep Contextualized Language Model
Pretrained contextualized language models such as BERT have achieved
impressive results on various natural language processing benchmarks.
Benefiting from multiple pretraining tasks and large scale training corpora,
pretrained models can capture complex syntactic word relations. In this paper,
we use the deep contextualized language model BERT for the task of ad hoc table
retrieval. We investigate how to encode table content considering the table
structure and input length limit of BERT. We also propose an approach that
incorporates features from prior literature on table retrieval and jointly
trains them with BERT. In experiments on public datasets, we show that our best
approach can outperform the previous state-of-the-art method and BERT baselines
with a large margin under different evaluation metrics.Comment: Accepted at SIGIR 2020 (Long
Mining Implicit Relevance Feedback from User Behavior for Web Question Answering
Training and refreshing a web-scale Question Answering (QA) system for a
multi-lingual commercial search engine often requires a huge amount of training
examples. One principled idea is to mine implicit relevance feedback from user
behavior recorded in search engine logs. All previous works on mining implicit
relevance feedback target at relevance of web documents rather than passages.
Due to several unique characteristics of QA tasks, the existing user behavior
models for web documents cannot be applied to infer passage relevance. In this
paper, we make the first study to explore the correlation between user behavior
and passage relevance, and propose a novel approach for mining training data
for Web QA. We conduct extensive experiments on four test datasets and the
results show our approach significantly improves the accuracy of passage
ranking without extra human labeled data. In practice, this work has proved
effective to substantially reduce the human labeling cost for the QA service in
a global commercial search engine, especially for languages with low resources.
Our techniques have been deployed in multi-language services.Comment: Accepted by KDD 202
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale
We study the zero-shot transfer capabilities of text matching models on a
massive scale, by self-supervised training on 140 source domains from community
question answering forums in English. We investigate the model performances on
nine benchmarks of answer selection and question similarity tasks, and show
that all 140 models transfer surprisingly well, where the large majority of
models substantially outperforms common IR baselines. We also demonstrate that
considering a broad selection of source domains is crucial for obtaining the
best zero-shot transfer performances, which contrasts the standard procedure
that merely relies on the largest and most similar domains. In addition, we
extensively study how to best combine multiple source domains. We propose to
incorporate self-supervised with supervised multi-task learning on all
available source domains. Our best zero-shot transfer model considerably
outperforms in-domain BERT and the previous state of the art on six benchmarks.
Fine-tuning of our model with in-domain data results in additional large gains
and achieves the new state of the art on all nine benchmarks.Comment: EMNLP-202