ymoslem/acl-abstracts huggingface dataset:
Using Extracted Emotion Cause to Improve Content-Relevance for Empathetic Conversation Generation (2022)

{\textquotedblleft}Empathetic conversation generation intends to endow the open-domain conversation model with the capability for understanding, interpreting, and expressing emotion. Humans express not only their emotional state but also the stimulus that caused the emotion, i.e., emotion cause, during a conversation. Most existing approaches focus on emotion modeling, emotion recognition and prediction, and emotion fusion generation, ignoring the critical aspect of the emotion cause, which results in generating responses with irrelevant content. Emotion cause can help the model understand the user`s emotion and make the generated responses more content-relevant. However, using the emotion cause to enhance empathetic conversation generation is challenging. Firstly, the model needs to accurately identify the emotion cause without large-scale labeled data. Second, the model needs to effectively integrate the emotion cause into the generation process. To this end, we present an emotion cause extractor using a semi-supervised training method and an empathetic conversation generator using a biased self-attention mechanism to overcome these two issues. Experimental results indicate that our proposed emotion cause extractor improves recall scores markedly compared to the baselines, and the proposed empathetic conversation generator has superior performance and improves the content-relevance of generated responses.{\textquotedblright}
To Adapt or to Fine-tune: A Case Study on Abstractive Summarization (2022)

{\textquotedblleft}Recent advances in the field of abstractive summarization leverage pre-trained language models rather than train a model from scratch. However, such models are sluggish to train and accompanied by a massive overhead. Researchers have proposed a few lightweight alternatives such as smaller adapters to mitigate the drawbacks. Nonetheless, it remains uncertain whether using adapters benefits the task of summarization, in terms of improved efficiency without an unpleasant sacrifice in performance. In this work, we carry out multifaceted investigations on fine-tuning and adapters for summarization tasks with varying complexity: language, domain, and task transfer. In our experiments, fine-tuning a pre-trained language model generally attains a better performance than using adapters; the performance gap positively correlates with the amount of training data used. Notably, adapters exceed fine-tuning under extremely low-resource conditions. We further provide insights on multilinguality, model convergence, and robustness, hoping to shed light on the pragmatic choice of fine-tuning or adapters in abstractive summarization.{\textquotedblright}
MRC-based Medical NER with Multi-task Learning and Multi-strategies (2022)

{\textquotedblleft}Medical named entity recognition (NER), a fundamental task of medical information extraction, is crucial for medical knowledge graph construction, medical question answering, and automatic medical record analysis, etc. Compared with named entities (NEs) in general domain, medical named entities are usually more complex and prone to be nested. To cope with both flat NEs and nested NEs, we propose a MRC-based approach with multi-task learning and multi-strategies. NER can be treated as a sequence labeling (SL) task or a span boundary detection (SBD) task. We integrate MRC-CRF model for SL and MRC-Biaffine model for SBD into the multi-task learning architecture, and select the more efficient MRC-CRF as the final decoder. To further improve the model, we employ multi-strategies, including adaptive pre-training, adversarial training, and model stacking with cross validation. Experiments on both nested NER corpus CMeEE and flat NER corpus CCKS2019 show the effectiveness of the MRC-based model with multi-task learning and multi-strategies.{\textquotedblright}
A Multi-Gate Encoder for Joint Entity and Relation Extraction (2022)

{\textquotedblleft}Named entity recognition and relation extraction are core sub-tasks of relational triple extraction. Recent studies have used parameter sharing or joint decoding to create interaction between these two tasks. However, ensuring the specificity of task-specific traits while the two tasks interact properly is a huge difficulty. We propose a multi-gate encoder that models bidirectional task interaction while keeping sufficient feature specificity based on gating mechanism in this paper. Precisely, we design two types of independent gates: task gates to generate task-specific features and interaction gates to generate instructive features to guide the opposite task. Our experiments show that our method increases the state-of-the-art (SOTA) relation F1 scores on ACE04, ACE05 and SciERC datasets to 63.8{\%} (+1.3{\%}), 68.2{\%} (+1.4{\%}), 39.4{\%} (+1.0{\%}), respectively, with higher inference speed over previous SOTA model.{\textquotedblright}
Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning (2022)

{\textquotedblleft}Event Temporal Relation Classification (ETRC) is crucial to natural language understanding. In recent years, the mainstream ETRC methods may not take advantage of lots of semantic information contained in golden temporal relation labels, which is lost by the discrete one-hot labels. To alleviate the loss of semantic information, we propose learning Temporal semantic information of the golden labels by Auxiliary Contrastive Learning (TempACL). Different from traditional contrastive learning methods, which further train the PreTrained Language Model (PTLM) with unsupervised settings before fine-tuning on target tasks, we design a supervised contrastive learning framework and make three improvements. Firstly, we design a new data augmentation method that generates augmentation data via matching templates established by us with golden labels. Secondly, we propose patient contrastive learning and design three patient strategies. Thirdly we design a label-aware contrastive learning loss function. Extensive experimental results show that our TempACL effectively adapts contrastive learning to supervised learning tasks which remain a challenge in practice. TempACL achieves new state-of-the-art results on TB-Dense and MATRES and outperforms the baseline model with up to 5.37{\%}F1 on TB-Dense and 1.81{\%}F1 on MATRES.{\textquotedblright}
Towards Making the Most of Pre-trained Translation Model for Quality Estimation (2022)

{\textquotedblleft}Machine translation quality estimation (QE) aims to evaluate the quality of machine translation automatically without relying on any reference. One common practice is applying the translation model as a feature extractor. However, there exist several discrepancies between the translation model and the QE model. The translation model is trained in an autoregressive manner, while the QE model is performed in a non-autoregressive manner. Besides, the translation model only learns to model human-crafted parallel data, while the QE model needs to model machinetranslated noisy data. In order to bridge these discrepancies, we propose two strategies to posttrain the translation model, namely Conditional Masked Language Modeling (CMLM) and Denoising Restoration (DR). Specifically, CMLM learns to predict masked tokens at the target side conditioned on the source sentence. DR firstly introduces noise to the target side of parallel data, and the model is trained to detect and recover the introduced noise. Both strategies can adapt the pre-trained translation model to the QE-style prediction task. Experimental results show that our model achieves impressive results, significantly outperforming the baseline model, verifying the effectiveness of our proposed methods.{\textquotedblright}
Supervised Contrastive Learning for Cross-lingual Transfer Learning (2022)

{\textquotedblleft}Multilingual pre-trained representations are not well-aligned by nature, which harms their performance on cross-lingual tasks. Previous methods propose to post-align the multilingual pretrained representations by multi-view alignment or contrastive learning. However, we argue that both methods are not suitable for the cross-lingual classification objective, and in this paper we propose a simple yet effective method to better align the pre-trained representations. On the basis of cross-lingual data augmentations, we make a minor modification to the canonical contrastive loss, to remove false-negative examples which should not be contrasted. Augmentations with the same class are brought close to the anchor sample, and augmentations with different class are pushed apart. Experiment results on three cross-lingual tasks from XTREME benchmark show our method could improve the transfer performance by a large margin with no additional resource needed. We also provide in-detail analysis and comparison between different post-alignment strategies.{\textquotedblright}
Interactive Mongolian Question Answer Matching Model Based on Attention Mechanism in the Law Domain (2022)

{\textquotedblleft}Mongolian question answer matching task is challenging, since Mongolian is a kind of lowresource language and its complex morphological structures lead to data sparsity. In this work, we propose an Interactive Mongolian Question Answer Matching Model (IMQAMM) based on attention mechanism for Mongolian question answering system. The key parts of the model are interactive information enhancement and max-mean pooling matching. Interactive information enhancement contains sequence enhancement and multi-cast attention. Sequence enhancement aims to provide a subsequent encoder with an enhanced sequence representation, and multi-cast attention is designed to generate scalar features through multiple attention mechanisms. MaxMean pooling matching is to obtain the matching vectors for aggregation. Moreover, we introduce Mongolian morpheme representation to better learn the semantic feature. The model experimented on the Mongolian corpus, which contains question-answer pairs of various categories in the law domain. Experimental results demonstrate that our proposed Mongolian question answer matching model significantly outperforms baseline models.{\textquotedblright}
TCM-SD: A Benchmark for Probing Syndrome Differentiation via Natural Language Processing (2022)

{\textquotedblleft}Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapy that has spread and been applied worldwide. The unique TCM diagnosis and treatment system requires a comprehensive analysis of a patient`s symptoms hidden in the clinical record written in free text. Prior studies have shown that this system can be informationized and intelligentized with the aid of artificial intelligence (AI) technology, such as natural language processing (NLP). However, existing datasets are not of sufficient quality nor quantity to support the further development of data-driven AI technology in TCM. Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system{---}syndrome differentiation (SD){---}and we introduce the first public large-scale benchmark for SD, called TCM-SD. Our benchmark contains 54,152 real-world clinical records covering 148 syndromes. Furthermore, we collect a large-scale unlabelled textual corpus in the field of TCM and propose a domain-specific pre-trained language model, called ZYBERT. We conducted experiments using deep neural networks to establish a strong performance baseline, reveal various challenges in SD, and prove the potential of domain-specific pre-trained language model. Our study and analysis reveal opportunities for incorporating computer science and linguistics knowledge to explore the empirical validity of TCM theories.{\textquotedblright}
COMPILING: A Benchmark Dataset for Chinese Complexity Controllable Definition Generation (2022)

{\textquotedblleft}The definition generation task aims to generate a word`s definition within a specific context automatically. However, owing to the lack of datasets for different complexities, the definitions produced by models tend to keep the same complexity level. This paper proposes a novel task of generating definitions for a word with controllable complexity levels. Correspondingly, we introduce COMPILING, a dataset given detailed information about Chinese definitions, and each definition is labeled with its complexity levels. The COMPILING dataset includes 74,303 words and 106,882 definitions. To the best of our knowledge, it is the largest dataset of the Chinese definition generation task. We select various representative generation methods as baselines for this task and conduct evaluations, which illustrates that our dataset plays an outstanding role in assisting models in generating different complexity-level definitions. We believe that the COMPILING dataset will benefit further research in complexity controllable definition generation.{\textquotedblright}
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack (2022)

{\textquotedblleft}Explanations can increase the transparency of neural networks and make them more trustworthy. However, can we really trust explanations generated by the existing explanation methods? If the explanation methods are not stable enough, the credibility of the explanation will be greatly reduced. Previous studies seldom considered such an important issue. To this end, this paper proposes a new evaluation frame to evaluate the stability of current typical feature attribution explanation methods via textual adversarial attack. Our frame could generate adversarial examples with similar textual semantics. Such adversarial examples will make the original models have the same outputs, but make most current explanation methods deduce completely different explanations. Under this frame, we test five classical explanation methods and show their performance on several stability-related metrics. Experimental results show our evaluation is effective and could reveal the stability performance of existing explanation methods.{\textquotedblright}
Dynamic Negative Example Construction for Grammatical Error Correction using Contrastive Learning (2022)

{\textquotedblleft}Grammatical error correction (GEC) aims at correcting texts with different types of grammatical errors into natural and correct forms. Due to the difference of error type distribution and error density, current grammatical error correction systems may over-correct writings and produce a low precision. To address this issue, in this paper, we propose a dynamic negative example construction method for grammatical error correction using contrastive learning. The proposed method can construct sufficient negative examples with diverse grammatical errors, and can be dynamically used during model training. The constructed negative examples are beneficial for the GEC model to correct sentences precisely and suppress the model from over-correction. Experimental results show that our proposed method enhances model precision, proving the effectiveness of our method.{\textquotedblright}
SPACL: Shared-Private Architecture based on Contrastive Learning for Multi-domain Text Classification (2022)

{\textquotedblleft}With the development of deep learning in recent years, text classification research has achieved remarkable results. However, text classification task often requires a large amount of annotated data, and data in different fields often force the model to learn different knowledge. It is often difficult for models to distinguish data labeled in different domains. Sometimes data from different domains can even damage the classification ability of the model and reduce the overall performance of the model. To address these issues, we propose a shared-private architecture based on contrastive learning for multi-domain text classification which can improve both the accuracy and robustness of classifiers. Extensive experiments are conducted on two public datasets. The results of experiments show that the our approach achieves the state-of-the-art performance in multi-domain text classification.{\textquotedblright}
Low-Resource Named Entity Recognition Based on Multi-hop Dependency Trigger (2022)

{\textquotedblleft}This paper introduces DepTrigger, a simple and effective model in low-resource named entity recognition (NER) based on multi-hop dependency triggers. Dependency triggers refer to salient nodes relative to an entity in the dependency graph of a context sentence. Our main observation is that triggers generally play an important role in recognizing the location and the type of entity in a sentence. Instead of exploiting the manual labeling of triggers, we use the syntactic parser to annotate triggers automatically. We train DepTrigger using an independent model architectures which are Match Network encoder and Entity Recognition Network encoder. Compared to the previous model TriggerNER, DepTrigger outperforms for long sentences, while still maintain good performance for short sentences as usual. Our framework is significantly more cost-effective in real business.{\textquotedblright}
Fundamental Analysis based Neural Network for Stock Movement Prediction (2022)

{\textquotedblleft}Stock movements are influenced not only by historical prices, but also by information outside the market such as social media and news about the stock or related stock. In practice, news or prices of a stock in one day are normally impacted by different days with different weights, and they can influence each other. In terms of this issue, in this paper, we propose a fundamental analysis based neural network for stock movement prediction. First, we propose three new technical indicators based on raw prices according to the finance theory as the basic encode of the prices of each day. Then, we introduce a coattention mechanism to capture the sufficient context information between text and prices across every day within a time window. Based on the mutual promotion and influence of text and price at different times, we obtain more sufficient stock representation. We perform extensive experiments on the real-world StockNet dataset and the experimental results demonstrate the effectiveness of our method.{\textquotedblright}
Focus on FoCus: Is FoCus focused on Context, Knowledge and Persona? (2022)

Rather than continuing the conversation based on personalized or implicit information, the existing conversation system generates dialogue by focusing only on the superficial content. To solve this problem, FoCus was recently released. FoCus is a persona-knowledge grounded dialogue generation dataset that leverages Wikipedia`s knowledge and personal persona, focusing on the landmarks provided by Google, enabling user-centered conversation. However, a closer empirical study is needed since research in the field is still in its early stages. Therefore, we fling two research questions about FoCus. {\textquotedblleft}Is the FoCus whether for conversation or question answering?{\textquotedblright} to identify the structural problems of the dataset. {\textquotedblleft}Does the FoCus model do real knowledge blending?{\textquotedblright} to closely demonstrate that the model acquires actual knowledge. As a result of the experiment, we present that the FoCus model could not correctly blend the knowledge according to the input dialogue and that the dataset design is unsuitable for the multi-turn conversation.
Proto-Gen: An end-to-end neural generator for persona and knowledge grounded response generation (2022)

In this paper we detail the implementation of Proto-Gen, an end-to-end neural response generator capable of selecting appropriate persona and fact sentences from available options, and generating persona and fact grounded responses. Incorporating a novel interaction layer in an encoder-decoder architecture, Proto-Gen facilitates learning dependencies between facts, persona and the context, and outperforms existing baselines on the FoCus dataset for both the sub-tasks of persona and fact selection, and response generation. We further fine tune Proto-Gen`s hyperparameters, and share our results and findings.
Evaluating Agent Interactions Through Episodic Knowledge Graphs (2022)

We present a new method based on episodic Knowledge Graphs (eKGs) for evaluating (multimodal) conversational agents in open domains. This graph is generated by interpreting raw signals during conversation and is able to capture the accumulation of knowledge over time. We apply structural and semantic analysis of the resulting graphs and translate the properties into qualitative measures. We compare these measures with existing automatic and manual evaluation metrics commonly used for conversational agents. Our results show that our Knowledge-Graph-based evaluation provides more qualitative insights into interaction and the agent`s behavior.
PERSONACHATGEN: Generating Personalized Dialogues using GPT-3 (2022)

Recently, many prior works have made their own agents generate more personalized and engaging responses using personachat. However, since this dataset is frozen in 2018, the dialogue agents trained on this dataset would not know how to interact with a human who loves {\textquotedblleft}Wandavision.{\textquotedblright} One way to alleviate this problem is to create a large-scale dataset. In this work, we introduce the pipeline of creating personachatgen, which is comprised of three main components: Creating (1) profilegen, (2) Persona Set, and (3) personachatgen. To encourage GPT-3`s generation ability, we also defined a taxonomy of hierarchical persona category derived from social profiling taxonomy. To create the speaker consistent persona set, we propose a simple contradiction-based iterative sentence replacement algorithm, named CoNL. Moreover, to prevent GPT-3 generating harmful content, we presented two filtering pipelines, one each for profilegen and personachatgen. Through analyzing of personachatgen, we showed that GPT-3 can generate personalized dialogue containing diverse persona. Furthermore, we revealed a state-of-the-art Blender 90M trained on our dataset that leads to higher performance.
A Multi-Modal Dataset for Hate Speech Detection on Social Media: Case-study of Russia-Ukraine Conflict (2022)

This paper presents a new multi-modal dataset for identifying hateful content on social media, consisting of 5,680 text-image pairs collected from Twitter, labeled across two labels. Experimental analysis of the presented dataset has shown that understanding both modalities is essential for detecting these techniques. It is confirmed in our experiments with several state-of-the-art multi-modal models. In future work, we plan to extend the dataset in size. We further plan to develop new multi-modal models tailored explicitly to hate-speech detection, aiming for a deeper understanding of the text and image relation. It would also be interesting to perform experiments in a direction that explores what social entities the given hate speech tweet targets.
EventGraph: Event Extraction as Semantic Graph Parsing (2022)

Event extraction involves the detection and extraction of both the event triggers and the corresponding arguments. Existing systems often decompose event extraction into multiple subtasks, without considering their possible interactions. In this paper, we propose EventGraph, a joint framework for event extraction, which encodes events as graphs. We represent event triggers and arguments as nodes in a semantic graph. Event extraction therefore becomes a graph parsing problem, which provides the following advantages: 1) performing event detection and argument extraction jointly; 2) detecting and extracting multiple events from a piece of text; 3) capturing the complicated interaction between event arguments and triggers. Experimental results on ACE2005 show that our model is competitive to state-of-the-art systems and has substantially improved the results on argument extraction. Additionally, we create two new datasets from ACE2005 where we keep the entire text spans for event arguments, instead of just the head word(s). Our code and models will be released as open-source.