Taiwan

To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i.e. storylines) to guide story generation. Such prior works mostly employ end-to-end neural generation models to predict event sequences for a story. However, such generation models struggle to guarantee the narrative coherence of separate events due to the hallucination problem, and additionally the generated event sequences are often hard to control due to the end-to-end nature of the models. To address these challenges, we propose NGEP, an novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph and enhances generalisation ability through a neural event advisor. We conduct a range of experiments on multiple criteria, and the results demonstrate that our graph-based neural framework outperforms the state-of-the-art (SOTA) event planning approaches, considering both the performance of event sequence generation and the effectiveness on the downstream task of story generation.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.24/)

IJCNLP-AACL 2022

NGEP: A Graph-based Event Planning Framework for Story Generation

event planning

artificial intelligence

story generation

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). 

poster

Pre-trained language models such as KoBART often fail to generate perfect interrogative sentences when they are applied to Korean question generation. 
This is mainly due to the fact that the language models are trained with declarative sentences, but not with interrogative sentences. 
Therefore, this paper proposes a novel post-training of KoBART to enhance it for Korean question generation. 
The enhancement of KoBART is accomplished in three ways: 
(i) introduction of question infilling objective to KoBART to enforce it to focus more on the structure of interrogative sentences, 
(ii) augmentation of training data for question generation with another MRC data from AI-Hub to cope with the lack of training instances for post-training, 
(iii) introduction of Korean spacing objective to make KoBART understand the linguistic features of Korean. 
Since there is no standard data set for Korean question generation, this paper also proposes KorQuAD-QG, 
a new data set for this task, to verify the performance of the proposed post-training. 
Our code are publicly available at https://github.com/gminipark/post_training_qg.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.26/)

Post-Training with Interrogative Sentences for Enhancing BART-based Korean Question Generator

A bottleneck to developing Semantic Parsing (SP) models is the need for a large volume of human-labeled training data. Given the complexity and cost of human annotation for SP, labeled data is often scarce, particularly in multilingual settings. Large Language Models (LLMs) excel at SP given only a few examples, however LLMs are unsuitable for runtime systems which require low latency. In this work, we propose CLASP, a simple method to improve low-resource SP for moderate-sized models: we generate synthetic data from AlexaTM 20B to augment the training set for a model 40x smaller (500M parameters). We evaluate on two datasets in low-resource settings: English PIZZA, containing either 348 or 16 real examples, and mTOP cross-lingual zero-shot, where training data is available only in English, and the model must generalize to four new languages. On both datasets, we show significant improvements over strong baseline methods.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.56/)

CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.55/)

An Empirical Study of Pipeline vs. Joint approaches to Entity and Relation Extraction

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.14/)

An Effective Post-training Embedding Binarization Approach for Fast Online Top-K Passage Matching

Graph-based text representation focuses on how text documents are represented as graphs for exploiting dependency information between tokens and documents within a corpus. Despite the increasing interest in graph representation learning, there is limited research in exploring new ways for graph-based text representation, which is important in downstream natural language processing tasks. In this paper, we first propose a new heterogeneous word-character text graph that combines word. and character n-gram nodes together with document nodes, allowing us to better learn dependencies among these entities. Additionally, we propose two new graph-based neural models, WCTextGCN and WCTextGAT, for modeling our proposed text graph. Extensive experiments in text classification and automatic
text summarization benchmarks demonstrate that our proposed models consistently outperform competitive baselines and state-of-the-art graph-based models.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.29/)

Improving Graph-Based Text Representations with Character and Word Level N-grams

Machine learning systems have shown impressive performance across a range of natural language tasks. However, it has been hypothesized that these systems are prone to learning spurious correlations that may be present in the training data. Though these correlations will not impact in-domain performance, they are unlikely to generalize well to out-of-domain data, limiting the applicability of systems. This work examines this phenomenon on text classification tasks. Rather than artificially injecting features into the data, we demonstrate that real spurious correlations can be exploited by current state-of-the-art deep-learning systems. Specifically, we show that even when only 'stop' words are available at the input stage, it is possible to predict the class significantly better than random. Though it is shown that these stop words are not required for good in-domain performance, they can degrade the ability of the system to generalize well to out-of-domain data.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.11/)

Analyzing Biases to Spurious Correlations in Text Classification Tasks

Language models (LMs) trained on raw texts have no direct access to the physical world. Gordon and Van Durme (2013) point out that LMs can thus suffer from reporting bias: texts rarely report on common facts, instead focusing on the unusual aspects of a situation. If LMs are only trained on text corpora and naively memorise local co-occurrence statistics, they thus naturally would learn a biased view of the physical world. While prior studies have repeatedly verified that LMs of smaller scales (e.g., RoBERTa, GPT-2) amplify reporting bias, it remains unknown whether such trends continue when models are scaled up. We investigate reporting bias from the perspective of colour in larger language models (LLMs) such as PaLM and GPT-3. Specifically, we query LLMs for the typical colour of objects, which is one simple type of perceptually grounded physical common sense. Surprisingly, we find that LLMs significantly outperform smaller LMs in determining an object's typical colour and more closely track human judgments, instead of overfitting to surface patterns stored in texts. This suggests that very large models of language alone are able to overcome certain types of reporting bias that are characterized by local co-occurrences.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.27/)


Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour

We propose a benchmark to assess the capability of large language models to reason with conventional metaphors. Our benchmark combines the previously isolated topics of metaphor detection and commonsense reasoning into a single task that requires a model to make inferences by accurately selecting between the literal and metaphorical register. We examine the performance of state-of-the-art pre-trained models on binary-choice tasks and find a large discrepancy between the performance of small and very large models, going from chance to near-human level. We also analyse the largest model in a generative setting and find that although human performance is approached, careful multiple-shot prompting is required.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.46/)

MiQA: A Benchmark for Inference on Metaphorical Questions

We investigate in this paper how correlations between occupations and gendered-pronouns can be affected and changed by adding negation in bias probes, or changing the grammatical tense of the verbs in the probes. We use a set of simple bias probes in Norwegian and English, and perform 16 different probing analysis, using four Norwegian and four English pre-trained language models. We show that adding negation to probes does not have a considerable effect on the correlations between gendered-pronouns and occupations, supporting other works on negation in language models. We also show that altering the grammatical tense of verbs in bias probes do have some interesting effects on models' behaviours and correlations. We argue that we should take grammatical tense into account when choosing bias probes, and aggregating results across tenses might be a better representation of the existing correlations.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.53/)

Exploring the Effects of Negation and Grammatical Tense on Bias Probes

Being able to train Named Entity Recognition (NER) models for emerging topics is crucial for many real-world applications especially in the medical domain where new topics are continuously evolving out of the scope of existing models and datasets. For a realistic evaluation setup, we introduce a novel COVID-19 news NER dataset (COVIDNEWS-NER) and release 3000 entries of hand annotated strongly labelled sentences and 13000 auto-generated weakly labelled sentences. Besides the dataset, we propose CONTROSTER, a recipe to strategically combine weak and strong labels in improving NER in an emerging topic through transfer learning. We show the effectiveness of CONTROSTER on COVIDNEWS-NER while providing analysis on combining weak and strong labels for training. Our key findings are: (1) Using weak data to formulate an initial backbone before tuning on strong data outperforms methods trained on only strong or weak data. (2) A combination of out-of-domain and in-domain weak label training is crucial and can overcome saturation when being training on weak labels from a single source.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.60/)

NGEP: A Graph-based Event Planning Framework for Story Generation

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Post-Training with Interrogative Sentences for Enhancing BART-based Korean Question Generator

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