Google is tackling a fundamental challenge of Natural Language Processing (NLP) as it pertains to particular search queries and using the results for the generation of paragraph length answers to questions.
Open-domain long-form question answering (LFQA) is a fundamental challenge in natural language processing (NLP) that involves retrieving documents relevant to a given question and using them to generate an elaborate paragraph-length answer. While there has been remarkable recent progress in factoid open-domain question answering (QA), where a short phrase or entity is enough to answer a question, much less work has been done in the area of long-form question answering. LFQA is nevertheless an important task, especially because it provides a testbed to measure the factuality of generative text models. But, are current benchmarks and evaluation metrics really suitable for making progress on LFQA?
In “Hurdles to Progress in Long-form Question Answering” (to appear at NAACL 2021), we present a new system for open-domain long-form question answering that leverages two recent advances in NLP: 1) state-of-the-art sparse attention models, such as Routing Transformer (RT), which allow attention-based models to scale to long sequences, and 2) retrieval-based models, such as REALM, which facilitate retrievals of Wikipedia articles related to a given query. To encourage more factual grounding, our system combines information from several retrieved Wikipedia articles related to the given question before generating an answer. It achieves a new state of the art on ELI5, the only large-scale publicly available dataset for long-form question answering.
However, while our system tops the public leaderboard, we discover several troubling trends with the ELI5 dataset and its associated evaluation metrics. In particular, we find 1) little evidence that models actually use the retrievals on which they condition; 2) that trivial baselines (e.g., input copying) beat modern systems, like RAG / BART+DPR; and 3) that there is a significant train/validation overlap in the dataset. Our paper suggests mitigation strategies for each of these issues.
We test the model on long-form question answering using the ELI5 dataset, which is a part of the KILT benchmark, and is the only publicly available large-scale LFQA dataset. The KILT benchmark measures text retrievals using Precision (R-Prec) and text generation using ROUGE-L. The two scores are combined to give a KILT R-L score, which determines a model’s ranking on the leaderboard. We fine-tune the pre-trained RT model together with retrievals from c-REALM on the ELI5 dataset from KILT.
Our submission tops the KILT leaderboard for long-form question answering on ELI5 with a combined KILT R-L score of 2.36. It improves on the previous leaderboard entry of BART + DPR (KILT R-L score of 1.9), while having a similar number of parameters as the other models on the leaderboard. In terms of text generation quality, we see an improvement of +4.11, +5.78 and +9.14 Rouge-L over T5, BART + DPR and RAG, respectively.
We proposed a system for long form-question answering based on Routing Transformers and REALM, which tops the KILT leaderboard on ELI5. However, a detailed analysis reveals several issues with the benchmark that preclude using it to inform meaningful modelling advances. We hope that the community works together to solve these issues so that researchers can climb the right hills and make meaningful progress in this challenging but important task.
The Routing Transformer work has been a team effort involving Aurko Roy, Mohammad Saffar, Ashish Vaswani and David Grangier. The follow-up work on open-domain long-form question answering has been a collaboration involving Kalpesh Krishna, Aurko Roy and Mohit Iyyer. We wish to thank Vidhisha Balachandran, Niki Parmar and Ashish Vaswani for several helpful discussions, and the REALM team (Kenton Lee, Kelvin Guu, Ming-Wei Chang and Zora Tung) for help with their codebase and several useful discussions, which helped us improve our experiments. We are grateful to Tu Vu for help with the QQP classifier used to detect paraphrases in ELI5 train and test sets. We thank Jules Gagnon-Marchand and Sewon Min for suggesting useful experiments on checking ROUGE-L bounds. Finally we thank Shufan Wang, Andrew Drozdov, Nader Akoury and the rest of the UMass NLP group for helpful discussions and suggestions at various stages in the project.
The full article can be found here.