Counterfactual thinking describes a psychological phenomenon that people re-infer the possible re... more Counterfactual thinking describes a psychological phenomenon that people re-infer the possible results with different solutions about things that have already happened. It helps people to gain more experience from mistakes and thus to perform better in similar future tasks. This paper investigates the counterfactual thinking for agents to find optimal decision-making strategies in multi-agent reinforcement learning environments. In particular, we propose a multi-agent deep reinforcement learning model with a structure which mimics the human-psychological counterfactual thinking process to improve the competitive abilities for agents. To this end, our model generates several possible actions (intent actions) with a parallel policy structure and estimates the rewards and regrets for these intent actions based on its current understanding of the environment. Our model incorporates a scenario-based framework to link the estimated regrets with its inner policies. During the iterations, o...
2020 25th International Conference on Pattern Recognition (ICPR), 2021
Joint-event-extraction, which extracts structural information (i.e., entities or triggers of even... more Joint-event-extraction, which extracts structural information (i.e., entities or triggers of events) from unstructured real-world corpora, has attracted more and more research attention in natural language processing. Most existing works do not fully address the sparse co-occurrence relationships between entities and triggers, which loses this important information and thus deteriorates the extraction performance. To mitigate this issue, we first define the joint-event-extraction as a sequence-to-sequence labeling task with a tag set composed of tags of triggers and entities. Then, to incorporate the missing information in the aforementioned co-occurrence relationships, we propose a Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of either triggers or entities based on the type distribution of each other. Moreover, since the connected entities and triggers naturally form a heterogeneous information network (HIN), we leverage the latent pattern along meta-pat...
Generative commonsense reasoning which aims to empower machines to generate sentences with the ca... more Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sentences. One reason is that they rarely consider incorporating the knowledge graph which can provide rich relational information among the commonsense concepts. To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graphaugmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output. Moreover, KG-BART can leverage the graph attention to aggregate the rich concept semantics that enhances the model generalization on unseen concept sets. Experiments on benchmark CommonGen dataset verify the effectiven...
Code representation learning, which aims to encode the semantics of source code into distributed ... more Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code intelligence. Recently, many pre-trained language models for source code (e.g., CuBERT and CodeBERT) have been proposed to model the context of code and serve as a basis for downstream code intelligence tasks such as code search, code clone detection, and program translation. Current approaches typically consider the source code as a plain sequence of tokens, or inject the structure information (e.g., AST and dataflow) into the sequential model pre-training. To further explore the properties of programming languages, this paper proposes SYNCOBERT, a Syntax-guided multi-modal contrastive pre-training approach for better Code representations. Specially, we design two novel pre-training objectives originating from the symbolic and syntactic properties of source code, i.e., Identifier Prediction (IP) and AST Edge ...
Developing effective distributed representations of source code is fundamental yet challenging fo... more Developing effective distributed representations of source code is fundamental yet challenging for many software engineering tasks such as code clone detection, code search, code translation and transformation. However, current code embedding approaches that represent the semantic and syntax of code in a mixed way are less interpretable and the resulting embedding can not be easily generalized across programming languages. In this paper, we propose a disentangled code representation learning approach to separate the semantic from the syntax of source code under a multi-programming-language setting, obtaining better interpretability and generalizability. Specially, we design three losses dedicated to the characteristics of source code to enforce the disentanglement effectively. We conduct comprehensive experiments on a real-world dataset composed of programming exercises implemented by multiple solutions that are semantically identical but grammatically distinguished. The experimenta...
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
The non-autoregressive models have boosted the efficiency of neural machine translation through p... more The non-autoregressive models have boosted the efficiency of neural machine translation through parallelized decoding at the cost of effectiveness, when comparing with the autoregressive counterparts. In this paper, we claim that the syntactic and semantic structures among natural language are critical for non-autoregressive machine translation and can further improve the performance. However, these structures are rarely considered in existing non-autoregressive models. Inspired by this intuition, we propose to incorporate the explicit syntactic and semantic structures of languages into a non-autoregressive Transformer, for the task of neural machine translation. Moreover, we also consider the intermediate latent alignment within target sentences to better learn the long-term token dependencies. Experimental results on two real-world datasets (i.e., WMT14 En-De and WMT16 En-Ro) show that our model achieves a significantly faster speed, as well as keeps the translation quality when compared with several stateof-the-art non-autoregressive models.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Intent detection is one of the core components of goal-oriented dialog systems, and detecting out... more Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is attracting much attention to mitigate data scarcity, but OOS detection becomes even more challenging. In this paper, we present a simple yet effective approach, discriminative nearest neighbor classification with deep self-attention. Unlike softmax classifiers, we leverage BERTstyle pairwise encoding to train a binary classifier that estimates the best matched training example for a user input. We propose to boost the discriminative ability by transferring a natural language inference (NLI) model. Our extensive experiments on a large-scale multi-domain intent detection task show that our method achieves more stable and accurate in-domain and OOS detection accuracy than RoBERTa-based classifiers and embeddingbased nearest neighbor approaches. More notably, the NLI transfer enables our 10-shot model to perform competitively with 50-shot or even full-shot classifiers, while we can keep the inference time constant by leveraging a faster embedding retrieval model.
2019 IEEE International Conference on Data Mining (ICDM), Nov 1, 2019
Counterfactual thinking describes a psychological phenomenon that people re-infer the possible re... more Counterfactual thinking describes a psychological phenomenon that people re-infer the possible results with different solutions about things that have already happened. It helps people to gain more experience from mistakes and thus to perform better in similar future tasks. This paper investigates the counterfactual thinking for agents to find optimal decision-making strategies in multi-agent reinforcement learning environments. In particular, we propose a multi-agent deep reinforcement learning model with a structure which mimics the human-psychological counterfactual thinking process to improve the competitive abilities for agents. To this end, our model generates several possible actions (intent actions) with a parallel policy structure and estimates the rewards and regrets for these intent actions based on its current understanding of the environment. Our model incorporates a scenario-based framework to link the estimated regrets with its inner policies. During the iterations, our model updates the parallel policies and the corresponding scenario-based regrets for agents simultaneously. To verify the effectiveness of our proposed model, we conduct extensive experiments on two different environments with real-world applications. Experimental results show that counterfactual thinking can actually benefit the agents to obtain more accumulative rewards from the environments with fair information by comparing to their opponents while keeping high performing efficiency.
Nowadays, more and more customers browse and purchase products in favor of using mobile E-Commerc... more Nowadays, more and more customers browse and purchase products in favor of using mobile E-Commerce Apps such as Taobao and Amazon. Since merchants are usually inclined to describe redundant and over-informative product titles to attract attentions from customers, it is important to concisely display short product titles on limited screen of mobile phones. To address this discrepancy, previous studies mainly consider textual information of long product titles and lacks of human-like view during training and evaluation process. In this paper, we propose a Multi-Modal Generative Adversarial Network (MM-GAN) for short product title generation in E-Commerce, which innovatively incorporates image information and attribute tags from product, as well as textual information from original long titles. MM-GAN poses short title generation as a reinforcement learning process, where the generated titles are evaluated by the discriminator in a human-like view. Extensive experiments on a large-scale E-Commerce dataset demonstrate that our algorithm outperforms other state-of-the-art methods. Moreover, we deploy our model into a real-world online E-Commerce environment and effectively boost the performance of click through rate and click conversion rate by 1.66% and 1.87%, respectively.
2015 IEEE International Conference on Web Services, 2015
Application Programming Interfaces (APIs) 1 , which are emerging web services in general, are inc... more Application Programming Interfaces (APIs) 1 , which are emerging web services in general, are increasing with a rapid speed in recent years. With so many APIs, many management platforms have been developed and deployed, leading to the boom of API markets, that are similar to the mobile App markets. Meanwhile, it has become more and more difficult to select and manage APIs. In reality, most existing management platforms typically recommend currently popular APIs to developers 2. However, the fact that popularity of API varies over time is ignored in those platforms, leading to the difficulty of recommending APIs that are just released but may be popular in the near future. To tackle this challenge, an approach of predicting the popularity of APIs is proposed in this paper. Predicting the popularity of API can not only be used for API ranking, recommendation and selection, but also make it more convenient for API providers and consumers to manage or select API respectively. In this paper, we propose a time-aware linear model to predict the API popularity, using time series feature of APIs and API's self-features such as its' provider ranking and description features, which are called heterogeneous features in our paper. Comprehensive experiments have been conducted on a real-world ProgrammableWeb dataset with 613 real APIs. The experimental results show that our model has a better performance, when compared with some other state-of-the-art prediction models.
Counterfactual thinking describes a psychological phenomenon that people re-infer the possible re... more Counterfactual thinking describes a psychological phenomenon that people re-infer the possible results with different solutions about things that have already happened. It helps people to gain more experience from mistakes and thus to perform better in similar future tasks. This paper investigates the counterfactual thinking for agents to find optimal decision-making strategies in multi-agent reinforcement learning environments. In particular, we propose a multi-agent deep reinforcement learning model with a structure which mimics the human-psychological counterfactual thinking process to improve the competitive abilities for agents. To this end, our model generates several possible actions (intent actions) with a parallel policy structure and estimates the rewards and regrets for these intent actions based on its current understanding of the environment. Our model incorporates a scenario-based framework to link the estimated regrets with its inner policies. During the iterations, o...
2020 25th International Conference on Pattern Recognition (ICPR), 2021
Joint-event-extraction, which extracts structural information (i.e., entities or triggers of even... more Joint-event-extraction, which extracts structural information (i.e., entities or triggers of events) from unstructured real-world corpora, has attracted more and more research attention in natural language processing. Most existing works do not fully address the sparse co-occurrence relationships between entities and triggers, which loses this important information and thus deteriorates the extraction performance. To mitigate this issue, we first define the joint-event-extraction as a sequence-to-sequence labeling task with a tag set composed of tags of triggers and entities. Then, to incorporate the missing information in the aforementioned co-occurrence relationships, we propose a Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of either triggers or entities based on the type distribution of each other. Moreover, since the connected entities and triggers naturally form a heterogeneous information network (HIN), we leverage the latent pattern along meta-pat...
Generative commonsense reasoning which aims to empower machines to generate sentences with the ca... more Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sentences. One reason is that they rarely consider incorporating the knowledge graph which can provide rich relational information among the commonsense concepts. To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graphaugmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output. Moreover, KG-BART can leverage the graph attention to aggregate the rich concept semantics that enhances the model generalization on unseen concept sets. Experiments on benchmark CommonGen dataset verify the effectiven...
Code representation learning, which aims to encode the semantics of source code into distributed ... more Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code intelligence. Recently, many pre-trained language models for source code (e.g., CuBERT and CodeBERT) have been proposed to model the context of code and serve as a basis for downstream code intelligence tasks such as code search, code clone detection, and program translation. Current approaches typically consider the source code as a plain sequence of tokens, or inject the structure information (e.g., AST and dataflow) into the sequential model pre-training. To further explore the properties of programming languages, this paper proposes SYNCOBERT, a Syntax-guided multi-modal contrastive pre-training approach for better Code representations. Specially, we design two novel pre-training objectives originating from the symbolic and syntactic properties of source code, i.e., Identifier Prediction (IP) and AST Edge ...
Developing effective distributed representations of source code is fundamental yet challenging fo... more Developing effective distributed representations of source code is fundamental yet challenging for many software engineering tasks such as code clone detection, code search, code translation and transformation. However, current code embedding approaches that represent the semantic and syntax of code in a mixed way are less interpretable and the resulting embedding can not be easily generalized across programming languages. In this paper, we propose a disentangled code representation learning approach to separate the semantic from the syntax of source code under a multi-programming-language setting, obtaining better interpretability and generalizability. Specially, we design three losses dedicated to the characteristics of source code to enforce the disentanglement effectively. We conduct comprehensive experiments on a real-world dataset composed of programming exercises implemented by multiple solutions that are semantically identical but grammatically distinguished. The experimenta...
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
The non-autoregressive models have boosted the efficiency of neural machine translation through p... more The non-autoregressive models have boosted the efficiency of neural machine translation through parallelized decoding at the cost of effectiveness, when comparing with the autoregressive counterparts. In this paper, we claim that the syntactic and semantic structures among natural language are critical for non-autoregressive machine translation and can further improve the performance. However, these structures are rarely considered in existing non-autoregressive models. Inspired by this intuition, we propose to incorporate the explicit syntactic and semantic structures of languages into a non-autoregressive Transformer, for the task of neural machine translation. Moreover, we also consider the intermediate latent alignment within target sentences to better learn the long-term token dependencies. Experimental results on two real-world datasets (i.e., WMT14 En-De and WMT16 En-Ro) show that our model achieves a significantly faster speed, as well as keeps the translation quality when compared with several stateof-the-art non-autoregressive models.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Intent detection is one of the core components of goal-oriented dialog systems, and detecting out... more Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is attracting much attention to mitigate data scarcity, but OOS detection becomes even more challenging. In this paper, we present a simple yet effective approach, discriminative nearest neighbor classification with deep self-attention. Unlike softmax classifiers, we leverage BERTstyle pairwise encoding to train a binary classifier that estimates the best matched training example for a user input. We propose to boost the discriminative ability by transferring a natural language inference (NLI) model. Our extensive experiments on a large-scale multi-domain intent detection task show that our method achieves more stable and accurate in-domain and OOS detection accuracy than RoBERTa-based classifiers and embeddingbased nearest neighbor approaches. More notably, the NLI transfer enables our 10-shot model to perform competitively with 50-shot or even full-shot classifiers, while we can keep the inference time constant by leveraging a faster embedding retrieval model.
2019 IEEE International Conference on Data Mining (ICDM), Nov 1, 2019
Counterfactual thinking describes a psychological phenomenon that people re-infer the possible re... more Counterfactual thinking describes a psychological phenomenon that people re-infer the possible results with different solutions about things that have already happened. It helps people to gain more experience from mistakes and thus to perform better in similar future tasks. This paper investigates the counterfactual thinking for agents to find optimal decision-making strategies in multi-agent reinforcement learning environments. In particular, we propose a multi-agent deep reinforcement learning model with a structure which mimics the human-psychological counterfactual thinking process to improve the competitive abilities for agents. To this end, our model generates several possible actions (intent actions) with a parallel policy structure and estimates the rewards and regrets for these intent actions based on its current understanding of the environment. Our model incorporates a scenario-based framework to link the estimated regrets with its inner policies. During the iterations, our model updates the parallel policies and the corresponding scenario-based regrets for agents simultaneously. To verify the effectiveness of our proposed model, we conduct extensive experiments on two different environments with real-world applications. Experimental results show that counterfactual thinking can actually benefit the agents to obtain more accumulative rewards from the environments with fair information by comparing to their opponents while keeping high performing efficiency.
Nowadays, more and more customers browse and purchase products in favor of using mobile E-Commerc... more Nowadays, more and more customers browse and purchase products in favor of using mobile E-Commerce Apps such as Taobao and Amazon. Since merchants are usually inclined to describe redundant and over-informative product titles to attract attentions from customers, it is important to concisely display short product titles on limited screen of mobile phones. To address this discrepancy, previous studies mainly consider textual information of long product titles and lacks of human-like view during training and evaluation process. In this paper, we propose a Multi-Modal Generative Adversarial Network (MM-GAN) for short product title generation in E-Commerce, which innovatively incorporates image information and attribute tags from product, as well as textual information from original long titles. MM-GAN poses short title generation as a reinforcement learning process, where the generated titles are evaluated by the discriminator in a human-like view. Extensive experiments on a large-scale E-Commerce dataset demonstrate that our algorithm outperforms other state-of-the-art methods. Moreover, we deploy our model into a real-world online E-Commerce environment and effectively boost the performance of click through rate and click conversion rate by 1.66% and 1.87%, respectively.
2015 IEEE International Conference on Web Services, 2015
Application Programming Interfaces (APIs) 1 , which are emerging web services in general, are inc... more Application Programming Interfaces (APIs) 1 , which are emerging web services in general, are increasing with a rapid speed in recent years. With so many APIs, many management platforms have been developed and deployed, leading to the boom of API markets, that are similar to the mobile App markets. Meanwhile, it has become more and more difficult to select and manage APIs. In reality, most existing management platforms typically recommend currently popular APIs to developers 2. However, the fact that popularity of API varies over time is ignored in those platforms, leading to the difficulty of recommending APIs that are just released but may be popular in the near future. To tackle this challenge, an approach of predicting the popularity of APIs is proposed in this paper. Predicting the popularity of API can not only be used for API ranking, recommendation and selection, but also make it more convenient for API providers and consumers to manage or select API respectively. In this paper, we propose a time-aware linear model to predict the API popularity, using time series feature of APIs and API's self-features such as its' provider ranking and description features, which are called heterogeneous features in our paper. Comprehensive experiments have been conducted on a real-world ProgrammableWeb dataset with 613 real APIs. The experimental results show that our model has a better performance, when compared with some other state-of-the-art prediction models.
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