The paper describes a transformer-based system designed for SemEval-2023 Task
9: Multilingual Tweet Intimacy Analysis. The purpose of the task was to predict
the intimacy of tweets in a range from 1 (not intimate at all) to 5 (very
intimate). The official training set for the competition consisted of tweets in
six languages (English, Spanish, Italian, Portuguese, French, and Chinese). The
test set included the given six languages as well as external data with four
languages not presented in the training set (Hindi, Arabic, Dutch, and Korean).
We presented a solution based on an ensemble of XLM-T, a multilingual RoBERTa
model adapted to the Twitter domain. To improve the performance of unseen
languages, each tweet was supplemented by its English translation. We explored
the effectiveness of translated data for the languages seen in fine-tuning
compared to unseen languages and estimated strategies for using translated data
in transformer-based models. Our solution ranked 4th on the leaderboard while
achieving an overall Pearson's r of 0.599 over the test set. The proposed
system improves up to 0.088 Pearson's r over a score averaged across all 45
submissions.

本文基于 XLM-T 提出了一种基于 transformer 的系统，用于预测多语言推特的亲密度，使用英语翻译数据可用于优化训练和推理，取得了 0.599 的总体 Pearson's r 值和排名第四的成绩。

使用 XLM-T、Google Translate 和集成学习的多语言推文亲密度检测在 SemEval-2023 任务 9 中的应用

tmn at SemEval-2023 Task 9: Multilingual Tweet Intimacy Detection using  XLM-T, Google Translate, and Ensemble Learning

We present the first end-to-end, transformer-based table question answering
(QA) system that takes natural language questions and massive table corpus as
inputs to retrieve the most relevant tables and locate the correct table cells
to answer the question. Our system, CLTR, extends the current state-of-the-art
QA over tables model to build an end-to-end table QA architecture. This system
has successfully tackled many real-world table QA problems with a simple,
unified pipeline. Our proposed system can also generate a heatmap of candidate
columns and rows over complex tables and allow users to quickly identify the
correct cells to answer questions. In addition, we introduce two new
open-domain benchmarks, E2E_WTQ and E2E_GNQ, consisting of 2,005 natural
language questions over 76,242 tables. The benchmarks are designed to validate
CLTR as well as accommodate future table retrieval and end-to-end table QA
research and experiments. Our experiments demonstrate that our system is the
current state-of-the-art model on the table retrieval task and produces
promising results for end-to-end table QA.

本研究提出了一种基于 Transformer 的端到端表格问答系统，可以将自然语言问题和大量表格语料库输入，以检索最相关的表格并定位正确的单元格以回答问题．该系统可以生成复杂表格上的候选列和行的热图，并允许用户快速标识回答问题的正确单元格．此外，研究提出了两个新的开放领域基准测试，E2E_WTQ 和 E2E_GNQ，旨在验证 CLTR 以及适用于未来的表格检索和端到端表格 QA 研究和实验．

CLTR: 一种基于 Transformer 的细胞级表格检索与表格问答系统

CLTR: An End-to-End, Transformer-Based System for Cell Level Table  Retrieval and Table Question Answering

Large amounts of data has made neural machine translation (NMT) a big success
in recent years. But it is still a challenge if we train these models on
small-scale corpora. In this case, the way of using data appears to be more
important. Here, we investigate the effective use of training data for
low-resource NMT. In particular, we propose a dynamic curriculum learning (DCL)
method to reorder training samples in training. Unlike previous work, we do not
use a static scoring function for reordering. Instead, the order of training
samples is dynamically determined in two ways - loss decline and model
competence. This eases training by highlighting easy samples that the current
model has enough competence to learn. We test our DCL method in a
Transformer-based system. Experimental results show that DCL outperforms
several strong baselines on three low-resource machine translation benchmarks
and different sized data of WMT' 16 En-De.

本研究提出一种动态课程学习方法，通过衡量模型的收益和能力来调整训练样本的顺序，从而使训练低资源神经机器翻译模型更加高效。在 Transformer-based 系统上的实验结果表明，该方法优于几个强基线，并适用于不同大小的 low-resource 机器翻译基准。