One noticeable trend in metaphor detection is the embrace of linguistic
theories such as the metaphor identification procedure (MIP) for model
architecture design. While MIP clearly defines that the metaphoricity of a
lexical unit is determined based on the contrast between its \textit{contextual
meaning} and its \textit{basic meaning}, existing work does not strictly follow
this principle, typically using the \textit{aggregated meaning} to approximate
the basic meaning of target words. In this paper, we propose a novel metaphor
detection method, which models the basic meaning of the word based on literal
annotation from the training set, and then compares this with the contextual
meaning in a target sentence to identify metaphors. Empirical results show that
our method outperforms the state-of-the-art method significantly by 1.0\% in F1
score. Moreover, our performance even reaches the theoretical upper bound on
the VUA18 benchmark for targets with basic annotations, which demonstrates the
importance of modelling basic meanings for metaphor detection.

提出了基于模型和训练集的字面注释，比较上下文意义以识别隐喻，并成功实现对基本注释目标的检测的一种新颖的隐喻检测方法，验证了对于隐喻检测建模基本意义的重要性和有效性。

通过显式基本含义建模实现隐喻检测

Metaphor Detection via Explicit Basic Meanings Modelling

The uniform information density (UID) hypothesis states that humans tend to
distribute information roughly evenly across an utterance or discourse. Early
evidence in support of the UID hypothesis came from Genzel & Charniak (2002),
which proposed an entropy rate constancy principle based on the probability of
English text under n-gram language models. We re-evaluate the claims of Genzel
& Charniak (2002) with neural language models, failing to find clear evidence
in support of entropy rate constancy. We conduct a range of experiments across
datasets, model sizes, and languages and discuss implications for the uniform
information density hypothesis and linguistic theories of efficient
communication more broadly.

通过神经语言模型重新评估 Genzel 和 Charniak 在信息熵速率恒定性原则方面的主张，未能发现明确支持熵速率恒定性的证据，对一致信息密度假设和有效交流的语言学理论的影响进行了实验研究。

探究文本熵速率不变性

Revisiting Entropy Rate Constancy in Text

Code-Switching, a common phenomenon in written text and conversation, has
been studied over decades by the natural language processing (NLP) research
community. Initially, code-switching is intensively explored by leveraging
linguistic theories and, currently, more machine-learning oriented approaches
to develop models. We introduce a comprehensive systematic survey on
code-switching research in natural language processing to understand the
progress of the past decades and conceptualize the challenges and tasks on the
code-switching topic. Finally, we summarize the trends and findings and
conclude with a discussion for future direction and open questions for further
investigation.

本文针对语言学与计算机科学领域的交叉学科问题 —— 代码切换现象进行了系统性的研究综述，归纳了过去几十年的研究进展、任务挑战、学术趋势和未来研究方向等。

自然语言处理中代码切换研究的几十年进展：趋势和挑战的系统调查

The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges

The recent dominance of distributional language representation models has
elicited a variety of questions regarding their capabilities and intrinsic
properties, one of which is the manifestation of compositional phenomena in
natural language, which has significant implications towards explainability and
safety/fairness in the use of such models. While most current research on
compositionality has been directed towards improving performance of the
representations on similarity tasks, this work proposes a methodology for
measuring the presence of compositional behaviour in contemporary language
models related to adjectival modifier phenomena in adjective-noun phrases. Our
results show that current neural language models do not behave consistently
according to the linguistic theories with regard to the evaluated intersective
property, but on the other hand, the differences between adjective categories
are noticeable in single adjective interactions, indicating that such
differences are encoded in individual word representations, but they do not
transfer generally in the expected way to the compositions. This raises the
question of whether current language models are not capable of capturing the
true underlying distributional properties of language, or whether linguistic
theories from Montagovian tradition do not hold to distributional scrutiny.

本研究提出一种新的方法来衡量当今语言模型中形容词修饰现象的组合表现，研究结果表明当代神经语言模型在考虑下文影响时存在显著的差异，并且词类之间存在差异但在组合中无法表现，这引起了当前语言模型是否能够捕捉到真正的语言分布特性的质疑。