Document Ranking with a Pretrained Sequence-to-Sequence Model (2003.06713v1)

Abstract: This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the task of document ranking. Our approach is fundamentally different from a commonly-adopted classification-based formulation of ranking, based on encoder-only pretrained transformer architectures such as BERT. We show how a sequence-to-sequence model can be trained to generate relevance labels as "target words", and how the underlying logits of these target words can be interpreted as relevance probabilities for ranking. On the popular MS MARCO passage ranking task, experimental results show that our approach is at least on par with previous classification-based models and can surpass them with larger, more-recent models. On the test collection from the TREC 2004 Robust Track, we demonstrate a zero-shot transfer-based approach that outperforms previous state-of-the-art models requiring in-dataset cross-validation. Furthermore, we find that our approach significantly outperforms an encoder-only model in a data-poor regime (i.e., with few training examples). We investigate this observation further by varying target words to probe the model's use of latent knowledge.

Document Ranking with a Pretrained Sequence-to-Sequence Model

The paper "Document Ranking with a Pretrained Sequence-to-Sequence Model" presents an approach to document ranking by leveraging a pretrained sequence-to-sequence model, diverging from the traditional classification-based formulations typically reliant on encoder-only architectures, such as BERT. It establishes a method by which a model generates relevance labels as "target words," with the logits of these target words offering probabilities usable for ranking documents.

Overview and Contributions

The authors extend the use of T5, a transformer-based sequence-to-sequence model, to document reranking tasks. Unlike prevalent BERT-like models that transform document ranking into a classification problem, T5 models harness the potential to interpret logits as relevance probabilities. This adaptation demonstrates efficacy that is at par with, or exceeds, existing classification models, particularly when operating with robust models or within data-constrained scenarios.

A notable experiment involves applying this model to MS MARCO, a standard passage-ranking benchmark. The results indicate that the proposed method achieves comparable results to classification-based models and, in some situations, provides superior performance. An additional zero-shot transfer experiment on the TREC 2004 Robust Track further underscores the approach's effectiveness, outperforming models finely tuned through in-dataset cross-validation.

Methodological Insights

The reranking mechanism uses an input format: "Query: [query] Document: [document] Relevant:", ultimately fine-tuning T5 to output "true" or "false," denoting document relevance. During inference, the model ranks documents by applying a softmax to "true" and "false" logits, thus computing relevance probabilities. This technique capitalizes on sequence-to-sequence model capabilities, preempting alternative methods which might use cumbersome logit aggregation strategies due to token segmentation.

Experimental Evaluation

Through a methodologically rigorous setup, the authors conduct experiments using two datasets: MS MARCO and TREC 2004 Robust Track. The results from MS MARCO underscore the model's capacity to perform competitively against BERT-based counterparts. On the Robust04 dataset, the zero-shot application revealed T5's considerable advantage, demonstrating unmatched performance in the literature. Furthermore, T5 is notably effective in low-data environments, showcasing significant efficacy with minimal training data compared to encoder-based models.

Discussion and Implications

The proposed formulation introduces a significant paradigmatic shift, suggesting that sequence-to-sequence models can compatibly map the model's latent representations to task-specific decisions while leveraging pretrained knowledge. This insight provides a strong basis for enhancing current document ranking systems, especially in scenarios where data is limited.

Furthermore, target word probing experiments reveal the model's capacity to exploit latent knowledge. Tests varying the semantic relatedness of target words indicate the model's dependency on pretrained linguistics and semantics, particularly in low-data regimes.

Future Directions

The findings call for deeper exploration into the sequence-to-sequence model's leveraging of pretrained knowledge for task-specific applications. Subsequent research might examine different architectures or further refine hyperparameters to optimize efficiency. Additionally, expanding this approach’s utility to other NLP tasks could uncover broader applications, heightening understanding of sequence-to-sequence dynamics in document ranking.

In summary, this paper introduces a novel generation-centric notion to document ranking, with demonstrable advances over classification models — an approach that could not only redefine established methodologies but also open avenues for future advancements in AI-driven information retrieval.