Taiwan

We describe initial work in developing a methodology for the automatic generation of a conversational agent or ‘chatbot’ through term and relation extraction from a relevant corpus of language data. We develop our approach in the domain of industrial heritage in the 18th and 19th centuries, and more specifically on the industrial history of canals and mills in Ireland. We collected a corpus of relevant newspaper reports and Wikipedia articles, which we deemed representative of a layman’s understanding of this topic. We used the Saffron toolkit to extract relevant terms and relations between the terms from the corpus and leveraged the extracted knowledge to query the British Library Digital Collection and the Project Gutenberg library. We leveraged the extracted terms and relations in identifying possible answers for a constructed set of questions based on the extracted terms, by matching them with sentences in the British Library Digital Collection and the Project Gutenberg library. In a final step, we then took this data set of question-answer pairs to train a chatbot. We evaluate our approach by manually assessing the appropriateness of the generated answers for a random sample, each of which is judged by four annotators.

IJCNLP-AACL 2022

Towards Bootstrapping a Chatbot on Industrial Heritage through Term and Relation Extraction

term extraction

digital humanities

relation extraction

dialogue systems

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

workshop paper

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). 

Emotion Conditioned Creative Dialog Generation

Integration of Named Entity Recognition and Sentence Segmentation on Ancient Chinese based on Siku-BERT

Emotional Intensity Estimation based on Writer's Personality

Automation of dialogue system evaluation is a driving force for the efficient development of dialogue systems. This paper introduces the bipartite-play method, a dialogue collection method for automating dialogue system evaluation. It addresses the limitations of existing dialogue collection methods: (i) inability to compare with systems that are not publicly available, and (ii) vulnerability to cheating by intentionally selecting systems to be compared. Experimental results show that the automatic evaluation using the bipartite-play method mitigates these two drawbacks and correlates as strongly with human subjectivity as existing methods.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-srw.2/)


Bipartite-play Dialogue Collection for Practical Automatic Evaluation of Dialogue Systems

The ability to capture temporal commonsense relationships for time-related events expressed in text is a very important task in natural language understanding. However, pre-trained language models such as BERT, which have recently achieved great success in a wide range of natural language processing tasks, are still considered to have poor performance in temporal reasoning. In this paper, we focus on the development of language models for temporal commonsense inference over several pre-trained language models. Our model relies on multi-step fine-tuning using multiple corpora and masked language modeling to predict masked temporal indicators that are crucial for temporal commonsense reasoning. We also experimented with multi-task learning and build a language model that can improve performance on multiple time-related tasks. In our experiments, multi-step fine-tuning using the general commonsense knowledge task as an auxiliary task produced the best results. We obtained a significant improvement in accuracy over standard fine-tuning in the temporal commonsense inference task and on other time-related tasks.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-srw.3/)


Toward Building a Language Model for Understanding Temporal Commonsense

In dialogue systems, one option for creating a better dialogue experience for the user is to have a human operator take over the dialogue when the system runs into trouble communicating with the user. In this type of handover situation (we call it intervention), it is useful for the operator to have access to the dialogue summary. However, it is not clear exactly what type of summary would be the most useful for a smooth handover. In this study, we investigated the optimal type of summary through experiments in which interlocutors were presented with various summary types during interventions in order to examine their effects. Our findings showed that the best summaries were an abstractive summary plus one utterance immediately before the handover and an extractive summary consisting of five utterances immediately before the handover. From the viewpoint of computational cost, we recommend that extractive summaries consisting of the last five utterances be used.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-srw.4/)


Optimal Summaries for Enabling a Smooth Handover in Chat-Oriented Dialogue

The exponential surge of social media has enabled information propagation at an unprecedented rate. However, it also led to the generation of a vast amount of malign content, such as hateful memes. To eradicate the detrimental impact of this content, over the last few years hateful memes detection problem has grabbed the attention of researchers. However, most past studies were conducted primarily for English memes, while memes on resource-constraint languages (i.e., Bengali)
remain under-studied. Moreover, current research considers memes with a caption written in monolingual (either English or Bengali) form. However, memes might have code-mixed captions (English+Bangla), and the existing models can not provide accurate inference in such cases. Therefore, to facilitate research in this arena, this paper introduces a multimodal hate speech dataset (named MUTE) consisting of 4158 memes having Bengali and code-mixed captions. A detailed annotation guideline is provided to aid the dataset creation in other resource-constraint languages. Additionally, extensive experiments have been carried out on MUTE, considering the only visual, only textual, and both modalities. The result demonstrates that joint evaluation of visual and textual features significantly improves (≈ 3%) the hateful memes classification compared to the unimodal evaluation.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-srw.5/)


MUTE: A Multimodal Dataset for Detecting Hateful Memes

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-srw.6/)


A Simple and Fast Strategy for Handling Rare Words in Neural Machine Translation

Writing computer programs is a skill that remains inaccessible to most due to the barrier of programming language (PL) syntax. While large language models (LLMs) have been proposed to translate natural language pseudocode to PL code, they are costly in terms of data and compute. We propose a lightweight alternative to LLMs that exploits the property of code wherein most tokens can be simply copied from the pseudocode. We divide the problem into three phases: Copy, Generate, and Combine. In the Copy Phase, a binary classifier is employed to determine and mask the pseudocode tokens that can be directly copied into the code. In the Generate Phase, a Sequence-to-Sequence model is used to generate the masked PL code equivalent. In the Combine Phase, the generated sequence is combined with the tokens that the Copy Phase had masked. We show that our C3PO models achieve similar performance to non-C3PO models while reducing the computational cost of training as well as the vocabulary sizes.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-srw.7/)

{C3PO}: {A} Lightweight Copying Mechanism for Translating Pseudocode to Code

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-srw.8/)

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