We present new supertaggers trained on HPSG-based treebanks. These treebanks
feature high-quality annotation based on a well-developed linguistic theory and
include diverse and challenging test datasets, beyond the usual WSJ section 23
and Wikipedia data. HPSG supertagging has previously relied on MaxEnt-based
models. We use SVM and neural CRF- and BERT-based methods and show that both
SVM and neural supertaggers achieve considerably higher accuracy compared to
the baseline. Our fine-tuned BERT-based tagger achieves 97.26% accuracy on 1000
sentences from WSJ23 and 93.88% on the completely out-of-domain The Cathedral
and the Bazaar (cb)). We conclude that it therefore makes sense to integrate
these new supertaggers into modern HPSG parsers, and we also hope that the
diverse and difficult datasets we used here will gain more popularity in the
field. We contribute the complete dataset reformatted for token classification.

我们提出了基于 HPSG 的新型超标注模型，通过 SVM 和神经网络方法在高质量标注的树库上进行训练，与基线相比，SVM 和神经超标注模型都取得了明显更高的准确性。我们的 BERT 超标注模型在 WSJ23 数据集和 Cathedral and the Bazaar 数据集上分别达到了 97.26% 和 93.88% 的准确率。因此，将这些新型超标注模型整合到现代 HPSG 解析器中是有意义的，并且希望我们在这里使用的多样而困难的数据集能在该领域中获得更多的关注。我们还提供了完整的数据集，格式化用于标记分类。

HPSG 超标记再审视

Revisiting Supertagging for HPSG

In many jurisdictions, the excessive workload of courts leads to high delays.
Suitable predictive AI models can assist legal professionals in their work, and
thus enhance and speed up the process. So far, Legal Judgment Prediction (LJP)
datasets have been released in English, French, and Chinese. We publicly
release a multilingual (German, French, and Italian), diachronic (2000-2020)
corpus of 85K cases from the Federal Supreme Court of Switzerland (FSCS). We
evaluate state-of-the-art BERT-based methods including two variants of BERT
that overcome the BERT input (text) length limitation (up to 512 tokens).
Hierarchical BERT has the best performance (approx. 68-70% Macro-F1-Score in
German and French). Furthermore, we study how several factors (canton of
origin, year of publication, text length, legal area) affect performance. We
release both the benchmark dataset and our code to accelerate future research
and ensure reproducibility.

通过多语言的法律案例数据集，使用 BERT 模型，成功预测了瑞士联邦最高法院的裁决案例，提高了司法工作效率。

Swiss-Judgment-Prediction: 多语言法律判决预测基准

Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction  Benchmark

Style is an integral part of natural language. However, evaluation methods
for style measures are rare, often task-specific and usually do not control for
content. We propose the modular, fine-grained and content-controlled
similarity-based STyle EvaLuation framework (STEL) to test the performance of
any model that can compare two sentences on style. We illustrate STEL with two
general dimensions of style (formal/informal and simple/complex) as well as two
specific characteristics of style (contrac'tion and numb3r substitution). We
find that BERT-based methods outperform simple versions of commonly used style
measures like 3-grams, punctuation frequency and LIWC-based approaches. We
invite the addition of further tasks and task instances to STEL and hope to
facilitate the improvement of style-sensitive measures.

提出模块化、细粒度和内容控制的基于相似度的 STEL 模型，用于测试比较两个句子风格的任何模型的性能，并发现基于 BERT 的方法优于其他常用的风格度量方法，希望加入更多任务和任务实例来促进风格敏感度量的改进。

一个基于模块化和相似性的语言风格评估框架：能否捕捉 STEL？

Does It Capture STEL? A Modular, Similarity-based Linguistic Style  Evaluation Framework

Biomedical entity linking aims to map biomedical mentions, such as diseases
and drugs, to standard entities in a given knowledge base. The specific
challenge in this context is that the same biomedical entity can have a wide
range of names, including synonyms, morphological variations, and names with
different word orderings. Recently, BERT-based methods have advanced the
state-of-the-art by allowing for rich representations of word sequences.
However, they often have hundreds of millions of parameters and require heavy
computing resources, which limits their applications in resource-limited
scenarios. Here, we propose a lightweight neural method for biomedical entity
linking, which needs just a fraction of the parameters of a BERT model and much
less computing resources. Our method uses a simple alignment layer with
attention mechanisms to capture the variations between mention and entity
names. Yet, we show that our model is competitive with previous work on
standard evaluation benchmarks.

本研究提出了一种轻量级神经方法，利用对齐层和注意力机制来捕捉提到与实体的不同名称变化，以解决生物医学概念链接中同一实体具有各种名称变体的挑战，并且在标准评估基准上证明该模型表现竞争力。