Taiwan

This paper is intended to study the effects of age of acquisition (AoA) and orthographic transparency on word retrieval in Persian, which is an understudied language. A naming task (both pictures and words) and a recall task (both pictures and words) were used to explore how lexical retrieval and verbal memory are affected by AoA and transparency. Seventy two native speakers of Persian were recruited to participate in two experiments. The results showed that early acquired words are processed faster than late acquired words only when pictures were used as stimuli. Transparency of the words was not an influential factor. However, in the recall experiment a three-way interaction was observed: early acquired pictures and words were processed faster than late acquired stimuli except the words in the transparent condition. The findings speak to the fact that language-specific properties of languages are very important.

IJCNLP-AACL 2022

Do Age of Acquisition and Orthographic Transparency Have the Same Effects in Different Modalities?

picture naming

recall

transparency

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The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

The distinction between mass nouns and count nouns has a long history in formal semantics, and linguists have been trying to identify the semantic properties defining the two classes. However, they also recognized that both can undergo meaning shifts and be used in contexts of a different type, via nominal coercion.
In this study, we present an approach to measure the meaning shift in count-mass coercion in English that makes use of static and contextualized word embedding distance. Our results show that the coercion shifts are detected only by a small subset of the traditional word embedding models and that the shifts detected by the contextualized embedding of BERT are more pronounced for mass nouns.

Exploring Nominal Coercion in Semantic Spaces with Static and Contextualized Word Embeddings.

We present a Japanese morphological corpus of sentences from 27 specialized domains for the two tasks of word segmentation and partof-speech tagging. Experiments on the corpus demonstrated that recent neural models with domain adaptation techniques and pretrained language models achieved accurate performance for the two tasks for many specialized domains

A Japanese Corpus of Many Specialized Domains for Word Segmentation and Part-of-Speech Tagging

Transformer-based language models are often trained on structured text where non-lexical markers of sentence and discourse structure (e.g., punctuation and casing) are present and used consistently. Transformers encode these markers and arguably benefit from the information they convey. Yet, a systematic evaluations of the contribution of non-lexical markers to model performance, and of whether models' behavior changes significantly in their absence, is currently lacking. This knowledge is both relevant from a theoretical standpoint, but also important to understand how well pretrained models may perform in common application scenarios where casing and punctuation are absent or inconsistent. 
Here, we analyze GPT-2's language modeling behavior in parallel corpora that differ in the presence vs. absence of consistent punctuation and casing. We compute GPT-2's precision and uncertainty in next-token prediction for multiple context sizes, and compare the resulting performance distributions across corpora. We find that absence of non-lexical markers, especially punctuation, increases model uncertainty, and it affects (but does not catastrophically disrupt) GPT-2's precision in next-token prediction. Interestingly, the absence of non-lexical markers prevents the model from benefiting from larger contexts in order to reduce the uncertainty of its predictions. Future work will extend this paradigm to a wider range of models and systematically investigate how features of training text affect both language modeling and downstream predictive performance.

Evaluating the role of non-lexical markers in GPT-2’s language modeling behavior

From COMET to COMES – Can Summary Evaluation Benefit from Translation Evaluation?

Phraseology studies have been enhanced by Corpus Linguistics, which has become an interdisciplinary field where current technologies play an important role in its development. Computational tools have been implemented in the last decades with positive results on the identification of phrases in different languages. One specific technology that has impacted these studies is social media. As researchers, we have turned our attention to collecting data from these platforms, which comes with great advantages and its own challenges. One of the challenges is the way we design and build corpora relevant to the questions emerging in this type of language expression. This has been approached from different angles, but one that has given invaluable outputs is the building of linguistic corpora with the use of online web applications. In this paper, we take a multidimensional approach to the collection, design, and deployment of a phraseology corpus for Latin American Spanish from Twitter data, extracting features using NLP techniques, and presenting it in an interactive online web application. We expect to contribute to the methodologies used for Corpus Linguistics in the current technological age. Finally, we make this tool publicly available to be used by any researcher interested in the data itself and also on the technological tools developed here.

Interactive Analysis and Visualisation of Annotated Collocations in Spanish (AVAnCES)

Fractality of sentiment arcs for literary quality assessment: The case of Nobel laureates

Determining the origin of a given passage in rabbinic ontologies such as medieval midrash collections is not always straightforward and is often a matter of dispute among scholars, yet it is essential for scholars' understanding of the passage and its relationship to other texts in the rabbinic corpus. 

To help solve this problem, we propose a system for classification of rabbinic literature based on its style, leveraging recently released pretrained Transformer models for Hebrew. By combining our model with a text reuse engine, we demonstrate how our method can be applied to uncover lost material from the Tanhuma-Yelammedenu literature.

Style Classification of Rabbinic Literature for Detection of Lost Midrash Tanhuma Material

Large pre-trained language models have brought remarkable progress in NLP. Pre-training and Fine-tuning have given state-of- art performance across tasks in text processing. Data Augmentation techniques have also helped build state-of-art models on low or zero resource tasks. Many works in the past have attempted at learning a single massively- multilingual machine translation model for zero-shot translation. Although those translation models are producing correct translations, the main challenge is those models are producing the wrong languages for zero-shot translation. This work and its results indicate that prompt conditioned large models do not suffer from off-target language errors i.e. errors arising due to translation to wrong languages. We empirically demonstrate the effectiveness of self-supervised pre-training and data augmentation for zero-shot multi-lingual machine translation.

MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation

In recent years, research on image caption generation has evolved to include not only the generation based on the information obtained from an image, but also the generation based on the user’s interest to image content by providing additional information called control signals. In this study, we propose a method that considers tracing an image as a control signal and reflects the user's intention in tracing in the generation of sentences. The proposed method is characterized by the fact that it not only generates a caption according to the object to be traced, but also considers the amount of time the user has traced a specific object in the image as a control signal that expresses the user's interest on the object. In order to generate sentences whose length is proportional to the tracing time, a non-autoregressive caption generation method is employed. Comparing the proposed method with existing methods for caption generation, the image caption generation that interactively reflects the intent of the explainer using the proposed method successfully generates captions that reflect the order and details of the explanation.

Caption Generation Reflecting the Intent of the Explainer by Tracing on an Image

We introduce a method for analyzing the relationship between internal representations of models and intermediate results by using simple math problems.

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Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs

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