Taiwan

Natural language understanding (NLU) tasks are typically defined by creating an annotated dataset in which each utterance is encountered once. Such data does not resemble real-world natural language interactions in which certain utterances are encountered frequently, others rarely.  For deployed NLU systems, this is a vital problem, since the underlying machine learning (ML) models are often fine-tuned on typical NLU data, in which utterance frequency is never factored in, and then applied to real-world data with a very different distribution.  Such systems need to maintain interpretation consistency for the high-frequency (head) utterances, while also doing well on low-frequency (tail) utterances. We propose an alternative strategy that explicitly uses utterance frequency in training data to learn models that are more robust to unknown distributions. We present a methodology to simulate utterance usage in two public corpora and create two new corpora with head, body and tail segments. We evaluate several methods for joint intent classification and named entity recognition (referred to as IC-NER), and propose to use two domain generalization (DG) approaches that we adapt to sequence labeling task. The DG approaches demonstrate up to 7.02% relative improvement in semantic accuracy over baselines on the tail data.  We provide insights as to why the proposed approaches work and show that the reasons for the observed improvements do not align with those reported in previous work.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.1/)

IJCNLP-AACL 2022

Chasing the Tail with Domain Generalization: A Case Study on Frequency-Enriched Datasets

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). 
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Registration Is Required

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). 

technical paper

Multiplex Networks and AI Unveil the Influence of the Mental Lexicon on Picture Naming and its Failures by People Struck with Aphasia

Object Naming is an important task within the field of Language and Vision that consists of generating a correct and appropriate name for an object given an image. The ManyNames dataset uses real-world human annotated images with multiple labels, instead of just one. In this work, we describe the adaptation of this dataset (originally in English) to Catalan, by (i) machine-translating the English labels and
(ii) collecting human annotations for a subset of the original corpus and comparing both resources. Analyses reveal divergences in the lexical variation of the two sets showing potential problems of directly translated resources, particularly when there is no resource to a proper context, which in this case is conveyed by the image. The analysis also points to the impact of cultural factors in the naming task, which
should be accounted for in future cross-lingual naming tasks.

CAT ManyNames: A New Dataset for Object Naming in Catalan.

The Thesaurus Linguae Latinae (TLL) is a comprehensive monolingual dictionary that records contextualized meanings and usages of Latin words in antique sources at an unprecedented scale. We created a new dataset based on a subset of sense representations in the TLL, with which we finetuned the Latin BERT neural language model (Bamman and Burns, 2020) on a supervised Word Sense Disambiguation task. We observe that the contextualized BERT representations finetuned on TLL data score better than static embeddings used in a bidirectional LSTM classifier on the same dataset, and that our per-headword BERT models achieve higher and more robust performance than reported by Bamman and Burns (2020) based on data from a bilingual Latin dictionary. We discuss the differences in sense organizational principles between these two lexical resources, and report about our dataset construction and improved evaluation methodology.

Finetuning Latin BERT for Word Sense Disambiguation on the Thesaurus Linguae Latinae.

Definition modeling is the task to generate a valid definition for a given input term. This relatively novel task has been approached either with no context (i.e., given a word embedding alone) and, more recently, as word-in-context modeling. Despite their success, most works make little to no distinction between resources and their specific features (e.g., type and style of definitions, or quality of examples) when used for training. Given the high diversity lexicographic resources exhibit in terms of topic coverage, style and formal structure, it is desirable for downstream definition modeling to better understand which of them are better suited for the task. In this paper, we propose an empirical evaluation of the well-known lexical database WordNet, and specifically, its dictionary examples. We evaluate them both directly, by matching them against criteria for good dictionary writing, and indirectly, in the task of definition modeling. Our results suggest that WordNet’s dictionary examples could be improved by extending them in length, and incorporating prototypicality.

Putting WordNet’s Dictionary Examples in the Context of Definition Modelling: An Empirical Analysis.

A Frame-Based Model of Inherent Polysemy, Copredication and Argument Coercion.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-tutorials.1/)


Efficient and Robust Knowledge Graph Construction

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-tutorials.3/)


When Cantonese NLP Meets Pre-training: Progress and Challenges

In this paper, we describe KreolMorisienMT, a dataset for benchmarking machine translation quality of Mauritian Creole. Mauritian Creole (Kreol Morisien) is a French-based creole and a lingua franca of the Republic of Mauritius. KreolMorisienMT consists of a parallel corpus between English and Kreol Morisien, French and Kreol Morisien and a monolingual corpus for Kreol Morisien. We first give an overview of Kreol Morisien and then describe the steps taken to create the corpora. Thereafter, we benchmark Kreol Morisien<-->English and Kreol Morisien<-->French models leveraging pre-trained models and multilingual transfer learning. Human evaluation reveals our systems' high translation quality.

KreolMorisienMT: A Dataset for Mauritian Creole  Machine Translation

Block diagrams are very popular for representing a workflow or process of a model. Understanding block diagrams by generating summaries can be extremely useful in document summarization. It can also assist people in inferring key insights from block diagrams without requiring a lot of perceptual and cognitive effort. In this paper, we propose a novel task of converting block diagram images into text by presenting a framework called “BloSum”. This framework extracts the contextual meaning from the images in the form of triplets that help the language model in summary generation. We also introduce a new dataset for complex computerized block diagrams, explain the dataset preparation process, and later analyze it. Additionally, to showcase the generalization of the model, we test our method with publicly available handwritten block diagram datasets. Our evaluation with different metrics demonstrates the effectiveness of our approach that outperforms other methods and techniques.

Block Diagram-to-Text: Understanding Block Diagram Images by Generating Natural Language Descriptors

Machine translation (MT) metrics often fail to achieve very high correlations with human assessments. In terms of MT system evaluation, most metrics pay equal attentions to every sample in an evaluation set, while in human evaluation, difficult sentences often make candidate systems distinguishable via notable fluctuations in human scores, especially when systems are competitive. We find that samples with high entropy values, which though usually count for less than 5%, tend to play a key role in MT evaluation: when the evaluation set is shrunk to only the high-entropy portion, correlations with human assessments are actually improved. Thus, in this paper, we propose a fast and unsupervised approach to enhance MT metrics using entropy, expanding the dimension of evaluation by introducing sentence-level difficulty. A translation hypothesis with a significantly high entropy value is considered difficult and receives a large weight in aggregation of system-level scores. Experimental results on five sub-tracks in the WMT19 Metrics shared tasks show that our proposed method significantly enhanced the performance of commonly-used MT metrics in terms of system-level correlations with human assessments, even outperforming existing SOTA metrics. In particular, all enhanced metrics exhibit overall stability in correlations with human assessments in circumstances where only competitive MT systems are included, while the corresponding standard metrics fail to correlate with human assessments.

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