Indonesia

Meta-learning is widely used for few-shot slot tagging in tasks of few-shot learning. The performance of existing methods is, however, seriously affected by sample forgetting issues, where the model forgets the historically learned meta-training tasks while solely relying on support sets when adapting to new tasks. To overcome this predicament, we propose the Memory-based Contrastive Meta-Learning (aka, MCML) method, including learn-from-the-memory and adaption-from-the-memory modules, which bridge the distribution gap between training episodes and between training and testing respectively. Specifically, the former uses an explicit memory bank to keep track of the label representations of previously trained episodes, with a contrastive constraint between the label representations in the current episode with the historical ones stored in the memory. In addition, the adaption-from-memory mechanism is introduced to learn more accurate and robust representations based on the shift between the same labels embedded in the testing episodes and memory. Experimental results show that the MCML outperforms several state-of-the-art methods on both SNIPS and NER datasets and demonstrates strong scalability with consistent improvement when the number of shots gets more.

IJCNLP-AACL 2023

MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

### Welcome to IJCNLP-AACL 2023!
I am honored and happy to welcome you to IJCNLP-AACL 2023! The conference hosts both the 13th
International Joint Conference on Natural Language Processing (IJCNLP) and the 3rd Conference of
the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL). 
The joint conferences of IJCNLP and AACL have been organized by alternating leadership in the
Asia-Pacific region, in odd years by Asian Federation of Natural Language Processing (AFNLP) and
in even years by AACL. The conference is organized by AFNLP this year. 

Considering the current situation that we are not yet fully over the coronavirus pandemic, the
conference is offered in a hybrid mode, off-line in Nusa Dua, Bali, Indonesia, and on-line with the help
of Underline, during November 1-4, 2023. For those attending off-line, I hope that you will find time to
explore the exciting local area, which remains quite warm and pleasant at this time of the year. For
those attending on-line, I hope that the attendance is not much hampered, sometimes even enhanced
and comfortable, though the on-line arrangements have been made without ample time. 

Message from Jong C. Park, Korea Advanced Institute of Science and Technology (KAIST) 
IJCNLP-AACL 2023 General Chair 
###### [Conference Handbook](https://drive.google.com/file/d/1S2JSXa105xJxIkNBfhYmGH20Om6wARBx/view?usp=sharing)

In order to acces this event page, you need to register [here](http://www.ijcnlp-aacl2023.org/)

The 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics

In neural code search, a Transformers-based pre-trained language model (such as CodeBERT) is used to embed both the query (NL) and the code snippet (PL) into a joint representation space; which is used to retrieve the relevant PLs satisfying the query. These models often make mistakes such as retrieving snippets with incorrect data types, and incorrect method names or signatures. The generalization ability beyond training data is also limited (as the code retrieval datasets vary in the ways NL-PL pairs are collected). In this work, we propose a novel contrastive learning technique (SYNC) that enables efficient finetuning of code LMs with soft and hard negatives, where the hard negatives are constructed using a set of structure-aware AST-based perturbations; targeted towards possible syntactic and semantic variations. Our method achieves significant improvements in retrieval performance for three code LMs (CodeBERT, GraphCodeBERT, UniXCoder) over four Python code retrieval datasets. We also open-source our [code](https://github.com/atharva-naik/SYNC) for reproducibility (https://github.com/atharva-naik/SYNC).

SYNC: A Structurally Guided Hard Negative Curricula for Generalizable Neural Code Search

Word sense disambiguation (WSD) is the task of determining the sense of a word in context. Translations have been used in WSD as a source of knowledge, and even as a means of delimiting word senses. In this paper, we define three theoretical properties of the relationship between senses and translations, and argue that they constitute necessary conditions for using translations as sense inventories. The key property of One Sense per Translation (OSPT) provides a foundation for a translation-based WSD method. The results of an intrinsic evaluation experiment indicate that our method achieves a precision of approximately 93% compared to manual corpus annotations. Our extrinsic evaluation experiments demonstrate WSD improvements of up to 4.6% F1-score on difficult WSD datasets.

One Sense per Translation

We explore zero- and few-shot generalization for fact verification (FV), which aims to generalize the FV model trained on well-resourced domains (e.g., Wikipedia) to low-resourced domains that lack human annotations. To this end, we first construct a benchmark dataset collection that contains 11 FV datasets representing 6 domains. We conduct an empirical analysis of generalization across these FV datasets, finding that current models generalize poorly. Our analysis reveals that several factors affect generalization, including dataset size, length of evidence, and the type of claims. Finally, we show that two directions of work improve generalization: 1) incorporating domain knowledge via pretraining on specialized domains, and 2) automatically generating training data via claim generation.

Investigating Zero- and Few-shot Generalization in Fact Verification

For sequence-to-sequence tasks it is challenging to combine individual system outputs. Further, there is also often a mismatch between the decoding criterion and the one used for assessment. Minimum Bayes' Risk (MBR) decoding can be used to combine system outputs in a manner that encourages better alignment with the final assessment criterion. This paper examines MBR decoding for Grammatical Error Correction (GEC) systems, where performance is usually evaluated in terms of edits and an associated F-score. Hence, we propose a novel MBR loss function directly linked to this form of criterion. Furthermore, an approach to expand the possible set of candidate sentences is described. This builds on a current max-voting combination scheme, as well as individual edit-level selection. Experiments on three popular GEC datasets and with state-of-the-art GEC systems demonstrate the efficacy of the proposed MBR approach. Additionally, the paper highlights how varying reward metrics within the MBR decoding framework can provide control over precision, recall, and the F-score in combined GEC systems.

Minimum Bayes' Risk Decoding for System Combination of Grammatical Error Correction Systems

The diversity and Zipfian frequency distribution of natural language predicates in corpora leads to sparsity in Entailment Graphs (EGs) built by Open Relation Extraction (ORE). EGs are computationally efficient and explainable models of natural language inference, but as symbolic models, they fail if a novel premise or hypothesis vertex is missing at test-time. We present theory and methodology for overcoming such sparsity in symbolic models. First, we introduce a theory of optimal smoothing of EGs by constructing transitive chains. We then demonstrate an efficient, open-domain, and unsupervised smoothing method using an off-the-shelf Language Model to find approximations of missing premise predicates. This improves recall by 25.1 and 16.3 percentage points on two difficult directional entailment datasets, while raising average precision and maintaining model explainability. Further, in a QA task we show that EG smoothing is most useful for answering questions with lesser supporting text, where missing premise predicates are more costly. Finally, controlled experiments with WordNet confirm our theory and show that hypothesis smoothing is difficult, but possible in principle.

Smoothing Entailment Graphs with Language Models

Recent advances of large pre-trained language models have motivated significant breakthroughs in various Text-to-SQL tasks. However, a number of challenges inhibit the deployment of SQL parsers in commercial applications. In this paper, we focus on two such challenges: decoding speed and multilingual input, and introduce FastRAT, a model that includes (i) a decoder-free framework to quickly generate SQL queries from natural language questions based on SQL Semantic Predictions, (ii) a cross-lingual multi-task pre-training scheme, and (iii) a method, based on distant supervision, to extend a semantic parser to new languages.

We apply FastRAT on CSpider and Spider, two challenging zero-shot semantic parsing benchmarks. Our system achieves an average of 10x decoding speedup over a set of competitive baselines based on auto- or semi-auto-regressive decoding. In the cross-lingual CSpider dataset, our approach achieves an exact query match accuracy score of 61.3, outperforming the relevant competition. In the monolingual task, it maintains competitive performance by exhibiting less than 5% accuracy drop compared to disproportionately slower solutions.

FastRAT: Fast and Efficient Cross-lingual Text-to-SQL Semantic Parsing

Bilingual Lexicon Induction (BLI), where words are translated between two languages, is an important NLP task. While noticeable progress on BLI in rich resource languages using static word embeddings has been achieved. The word translation performance can be further improved by incorporating information from contextualized word embeddings. In this paper, we introduce ProMap, a novel approach for BLI that leverages the power of prompting pretrained multilingual and multidialectal language models to address these challenges. To overcome the employment of subword tokens in these models, ProMap relies on an effective \textit{padded prompting} of language models with a seed dictionary that achieves good performance when used independently. We also demonstrate the effectiveness of ProMap in re-ranking results from other BLI methods such as with aligned static word embeddings. When evaluated on both rich-resource and low-resource languages, ProMap consistently achieves state-of-the-art results. Furthermore, ProMap enables strong performance in few-shot scenarios (even with less than 10 training examples), making it a valuable tool for low-resource language translation. Overall, we believe our method offers both exciting and promising direction for BLI in general and low-resource languages in particular. ProMap code and data are available at https://github.com/4mekki4/promap.

ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting

Lexical-semantic resources such as wordnets and multilingual dictionaries often suffer from significant coverage issues, especially in languages other than English. While improving their coverage manually is a prohibitively expensive undertaking, current approaches to the automatic creation of such resources fail to investigate the latest advances achieved in relevant fields, such as cross-lingual annotation projection. In this work, we address these shortcomings and propose LexicoMatic, a novel resource-independent approach to the automatic construction and expansion of multilingual semantic dictionaries, in which we formulate the task as an annotation projection problem. In addition, we tackle the lack of a comprehensive multilingual evaluation framework and put forward a new entirely manually-curated benchmark featuring 9 languages. We evaluate LexicoMatic with an extensive array of experiments and demonstrate the effectiveness of our approach, achieving a new state of the art across all languages under consideration. We release our novel evaluation benchmark at: https://github.com/SapienzaNLP/lexicomatic.

LexicoMatic: Automatic Creation of Multilingual Lexical-Semantic Dictionaries

While large pre-trained language models (LLMs) have shown their impressive capabilities in various NLP tasks, they are still under-explored in the misinformation domain. In this paper, we examine LLMs with in-context learning (ICL) for news claim verification, and find that only with 4-shot demonstration examples, the performance of several prompting methods can be comparable with previous supervised models. To further boost performance, we introduce a Hierarchical Step-by-Step (HiSS) prompting method which directs LLMs to separate a claim into several subclaims and then verify each of them via multiple questions-answering steps progressively.
Experiment results on two public misinformation datasets show that HiSS prompting outperforms state-of-the-art fully-supervised approach and strong few-shot ICL-enabled baselines.

Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method

Abduction has long been seen as crucial for narrative comprehension and reasoning about everyday situations. The abductive natural language inference (αNLI) task has been proposed, and this narrative text-based task aims to infer the most plausible hypothesis from the candidates given two observations. However, the inter-sentential coherence and the model consistency have not been well exploited in the previous works on this task. In this work, we propose a prompt tuning model α-PACE, which takes self-consistency and inter-sentential coherence into consideration. Besides, we propose a general self-consistent framework that considers various narrative sequences (e.g., linear narrative and reverse chronology) for guiding the pre-trained language model in understanding the narrative context of input. We conduct extensive experiments and thorough ablation studies to illustrate the necessity and effectiveness of α-PACE. The performance of our method shows significant improvement against extensive competitive baselines.

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