We present 1-Pager the first system that answers a question and retrieves
evidence using a single Transformer-based model and decoding process. 1-Pager
incrementally partitions the retrieval corpus using constrained decoding to
select a document and answer string, and we show that this is competitive with
comparable retrieve-and-read alternatives according to both retrieval and
answer accuracy metrics. 1-Pager also outperforms the equivalent closed-book
question answering model, by grounding predictions in an evidence corpus. While
1-Pager is not yet on-par with more expensive systems that read many more
documents before generating an answer, we argue that it provides an important
step toward attributed generation by folding retrieval into the
sequence-to-sequence paradigm that is currently dominant in NLP. We also show
that the search paths used to partition the corpus are easy to read and
understand, paving a way forward for interpretable neural retrieval.

1-Pager 是第一个使用单个基于 Transformer 模型和解码过程回答问题和检索证据的系统，通过使用约束解码来增量地分割检索语料库以选择文档和答案字符串，我们展示了其在检索和答案准确性指标上与可比较的检索和阅读替代方法相竞争。1-Pager 还通过在证据语料库中进行预测的方式胜过了同等的闭书问题回答模型，虽然 1-Pager 目前还不及读取更多文档后生成答案的更昂贵系统，但我们认为它为将检索折叠到目前在 NLP 中占主导地位的序列到序列范式中提供了重要的一步。我们还展示了用于分割语料库的搜索路径易于阅读和理解，为可解释的神经检索铺平了道路。

一次性答案生成和证据检索

1-PAGER: One Pass Answer Generation and Evidence Retrieval

Recent years witnessed several advances in developing multi-goal
conversational recommender systems (MG-CRS) that can proactively attract users'
interests and naturally lead user-engaged dialogues with multiple
conversational goals and diverse topics. Four tasks are often involved in
MG-CRS, including Goal Planning, Topic Prediction, Item Recommendation, and
Response Generation. Most existing studies address only some of these tasks. To
handle the whole problem of MG-CRS, modularized frameworks are adopted where
each task is tackled independently without considering their interdependencies.
In this work, we propose a novel Unified MultI-goal conversational recommeNDer
system, namely UniMIND. In specific, we unify these four tasks with different
formulations into the same sequence-to-sequence (Seq2Seq) paradigm.
Prompt-based learning strategies are investigated to endow the unified model
with the capability of multi-task learning. Finally, the overall learning and
inference procedure consists of three stages, including multi-task learning,
prompt-based tuning, and inference. Experimental results on two MG-CRS
benchmarks (DuRecDial and TG-ReDial) show that UniMIND achieves
state-of-the-art performance on all tasks with a unified model. Extensive
analyses and discussions are provided for shedding some new perspectives for
MG-CRS.

提出了一种名为 UniMIND 的基于序列到序列范例的统一多目标会话推荐系统，采用基于提示的学习策略进行多任务学习，在两种 MG-CRS 基准（DuRecDial 和 TG-ReDial）上取得了最先进的性能。

多目标会话推荐系统的统一多任务学习框架

A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems

Inspired by Curriculum Learning, we propose a consecutive (i.e.,
image-to-text-to-text) generation framework where we divide the problem of
radiology report generation into two steps. Contrary to generating the full
radiology report from the image at once, the model generates global concepts
from the image in the first step and then reforms them into finer and coherent
texts using a transformer architecture. We follow the transformer-based
sequence-to-sequence paradigm at each step. We improve upon the
state-of-the-art on two benchmark datasets.

提出一个基于课程学习的顺序（即，图像 - 文本 - 文本）生成框架，从而将放射学报告的生成问题分为两步。使用变压器体系结构，将图像以步骤一为全局概念生成，然后进行二次生成，提高了两个基准数据集的最新技术水平。