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Fen Lin

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7 papers

1 author row

AAAI Conference 2020 Conference Paper

Neural Snowball for Few-Shot Relation Learning

Tianyu Gao
Xu Han
Ruobing Xie
Zhiyuan Liu
Fen Lin
Leyu Lin
Maosong Sun

Knowledge graphs typically undergo open-ended growth of new relations. This cannot be well handled by relation extraction that focuses on pre-deﬁned relations with sufﬁcient training data. To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations. More speciﬁcally, we use Relational Siamese Networks (RSN) to learn the metric of relational similarities between instances based on existing relations and their labeled data. Afterwards, given a new relation and its few-shot instances, we use RSN to accumulate reliable instances from unlabeled corpora; these instances are used to train a relation classiﬁer, which can further identify new facts of the new relation. The process is conducted iteratively like a snowball. Experiments show that our model can gather high-quality instances for better fewshot relation learning and achieves signiﬁcant improvement compared to baselines. Codes and datasets are released on https: //github. com/thunlp/Neural-Snowball.

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IJCAI Conference 2018 Conference Paper

Adaboost with Auto-Evaluation for Conversational Models

Juncen Li
Ping Luo
Ganbin Zhou
Fen Lin
Cheng Niu

We propose a boosting method for conversational models to encourage them to generate more human-like dialogs. In our method, we consider existing conversational models as weak generators and apply Adaboost to update those models. However, conventional Adaboost cannot be directly applied on conversational models. Because for conversational models, conventional Adaboost cannot adaptively adjust the weight on the instance for subsequent learning, result from the simple comparison between the true output y (to an input x) and its corresponding predicted output y' cannot directly evaluate the learning performance on x. To address this issue, we develop the Adaboost with Auto-Evaluation (called AwE). In AwE, an auto-evaluator is proposed to evaluate the predicted results, which makes it applicable to conversational models. Furthermore, we present the theoretical analysis that the training error drops exponentially fast only if certain assumption over the proposed auto-evaluator holds. Finally, we empirically show that AwE visibly boosts the performance of existing single conversational models and also outperforms the other ensemble methods for conversational models.

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AAAI Conference 2018 Conference Paper

Conversational Model Adaptation via KL Divergence Regularization

Juncen Li
Ping Luo
Fen Lin
Bo Chen

In this study we formulate the problem of conversational model adaptation, where we aim to build a generative conversational model for a target domain based on a limited amount of dialogue data from this target domain and some existing dialogue models from related source domains. This model facilitates the fast building of a chatbot platform, where a new vertical chatbot with only a small number of conversation data can be supported by other related mature chatbots. Previous studies on model adaptation and transfer learning mostly focus on classiﬁcation and recommendation problems, however, how these models work for conversation generation are still un-explored. To this end, we leverage a KL divergence (KLD) regularization to adapt the existing conversational models. Speciﬁcally, it employs the KLD to measure the distance between source and target domain. Adding KLD as a regularization to the objective function allows the proposed method to utilize the information from source domains effectively. We also evaluate the performance of this adaptation model for the online chatbots in Wechat platform of public accounts using both the BLEU metric and human judgement. The experiments empirically show that the proposed method visibly improves these evaluation metrics.

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AAAI Conference 2018 Conference Paper

Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning With Confidence

Ruobing Xie
Zhiyuan Liu
Fen Lin
Leyu Lin

Knowledge graphs (KGs), which could provide essential relational information between entities, have been widely utilized in various knowledge-driven applications. Since the overall human knowledge is innumerable that still grows explosively and changes frequently, knowledge construction and update inevitably involve automatic mechanisms with less human supervision, which usually bring in plenty of noises and conﬂicts to KGs. However, most conventional knowledge representation learning methods assume that all triple facts in existing KGs share the same signiﬁcance without any noises. To address this problem, we propose a novel conﬁdence-aware knowledge representation learning framework (CKRL), which detects possible noises in KGs while learning knowledge representations with conﬁdence simultaneously. Speciﬁcally, we introduce the triple conﬁdence to conventional translation-based methods for knowledge representation learning. To make triple conﬁdence more ﬂexible and universal, we only utilize the internal structural information in KGs, and propose three kinds of triple con- ﬁdences considering both local and global structural information. In experiments, We evaluate our models on knowledge graph noise detection, knowledge graph completion and triple classiﬁcation. Experimental results demonstrate that our conﬁdence-aware models achieve signiﬁcant and consistent improvements on all tasks, which conﬁrms the capability of CKRL modeling conﬁdence with structural information in both KG noise detection and knowledge representation learning.

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AAAI Conference 2018 Conference Paper

Elastic Responding Machine for Dialog Generation with Dynamically Mechanism Selecting

Ganbin Zhou
Ping Luo
Yijun Xiao
Fen Lin
Bo Chen
Qing He

Neural models aiming at generating meaningful and diverse response is attracting increasing attention over recent years. For a given post, the conventional encoder-decoder models tend to learn high-frequency but trivial responses, or are dif- ﬁcult to determine which speaking styles are suitable to generate responses. To address this issue, we propose the elastic responding machine (ERM), which is based on a proposed encoder-diverter-ﬁlter-decoder framework. ERM models the multiple responding mechanisms to not only generate acceptable responses for a given post but also improve the diversity of responses. Here, the mechanisms could be regraded as some latent variables, and for a given post different responses may be generated by different mechanisms. The experiments demonstrate the quality and diversity of the generated responses, intuitively show how the learned model controls response mechanism when responding, and reveal some underlying relationship between mechanism and language style.

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AAAI Conference 2018 Conference Paper

Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation

Ganbin Zhou
Ping Luo
Rongyu Cao
Yijun Xiao
Fen Lin
Bo Chen
Qing He

Different from other sequential data, sentences in natural language are structured by linguistic grammars. Previous generative conversational models with chain-structured decoder ignore this structure in human language and might generate plausible responses with less satisfactory relevance and ﬂuency. In this study, we aim to incorporate the results from linguistic analysis into the process of sentence generation for high-quality conversation generation. Speciﬁcally, we use a dependency parser to transform each response sentence into a dependency tree and construct a training corpus of sentencetree pairs. A tree-structured decoder is developed to learn the mapping from a sentence to its tree, where different types of hidden states are used to depict the local dependencies from an internal tree node to its children. For training acceleration, we propose a tree canonicalization method, which transforms trees into equivalent ternary trees. Then, with a proposed tree-structured search method, the model is able to generate the most probable responses in the form of dependency trees, which are ﬁnally ﬂattened into sequences as the system output. Experimental results demonstrate that the proposed X2TREE framework outperforms baseline methods over 11. 15% increase of acceptance ratio.

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AAAI Conference 2017 Conference Paper

Mechanism-Aware Neural Machine for Dialogue Response Generation

Ganbin Zhou
Ping Luo
Rongyu Cao
Fen Lin
Bo Chen
Qing He

To the same utterance, people’s responses in everyday dialogue may be diverse largely in terms of content semantics, speaking styles, communication intentions and so on. Previous generative conversational models ignore these 1-to-n relationships between a post to its diverse responses, and tend to return high-frequency but meaningless responses. In this study we propose a mechanism-aware neural machine for dialogue response generation. It assumes that there exists some latent responding mechanisms, each of which can generate different responses for a single input post. With this assumption we model different responding mechanisms as latent embeddings, and develop a encoder-diverter-decoder framework to train its modules in an end-to-end fashion. With the learned latent mechanisms, for the ﬁrst time these decomposed modules can be used to encode the input into mechanism-aware context, and decode the responses with the controlled generation styles and topics. Finally, the experiments with human judgements, intuitive examples, detailed discussions demonstrate the quality and diversity of the generated responses with 9. 80% increase of acceptable ratio over the best of six baseline methods.

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