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Translated texts have been used for malicious purposes, i.e., plagiarism or fake reviews. Existing detectors have been built around a specific translator (e.g., Google) but fail to detect a translated text from a strange translator. If we use the same translator, the translated text is similar to its round-trip translation, which is when text is translated into another language and translated back into the original language. However, a round-trip translated text is significantly different from the original text or a translated text using a strange translator. Hence, we propose a detector using text similarity with round-trip translation (TSRT). TSRT achieves 86.9% accuracy in detecting a translated text from a strange translator. It outperforms existing detectors (77.9%) and human recognition (53.3%).

NAACL 2021

Machine Translated Text Detection Through Text Similarity with Round-Trip Translation

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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technical paper

Measuring document similarity plays an important role in natural language processing tasks. Most existing document similarity approaches suffer from the information gap caused by context and vocabulary mismatches when comparing varying-length texts. In this paper, we propose an unsupervised concept representation learning approach to address the above issues. Speciﬁcally, we propose a novel Concept Generation Network (CGNet) to learn concept representations from the perspective of the entire text corpus. Moreover, a concept-based document matching method is proposed to leverage advances in the recognition of local phrase features and corpuslevel concept features. Extensive experiments on real-world data sets demonstrate that new method can achieve a considerable improvement in comparing length-varying texts. In particular, our model achieved 6.5% better F1 Score compared to the best of the baseline models for a concept-project benchmark dataset.

Unsupervised Concept Representation Learning for Length-Varying Text Similarity

We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice’s Maxims of Conversation (1975). Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the ‘I don’tknow’ problem, in which a dialog system produces generic responses. The linguistically motivated RUQ diagnostic compares the model score of a generic response to that of the reference response. We find that for reasonable baseline models, ‘I don’t know’ is preferred over the reference the majority of the time, but this can be reduced to less than5% with hyperparameter tuning. RUQ allows for the direct analysis of the ‘I don’t know’ problem, which has been addressed but not analyzed by prior work.

Measuring the ‘I don’t know’ Problem through the Lens of Gricean Quantity

Generative models for dialog systems have gained much interest because of the recent success of RNN and Transformer based models in tasks like question answering and summarization. Although the task of dialog response generation is generally seen as a sequence-to-sequence (Seq2Seq) problem, researchers in the past have found it challenging to train dialog systems using the standard Seq2Seq models. Therefore, to help the model learn meaningful utterance and conversation level features, Sordoni et al. (2015b); Serban et al. (2016) proposed Hierarchical RNN architecture, which was later adopted by several other RNN based dialog systems. With the transformer-based models dominating the seq2seq problems lately, the natural question to ask is the applicability of the notion of hierarchy in transformer based dialog systems. In this paper, we propose a generalized framework for Hierarchical Transformer Encoders and show how a standard transformer can be morphed into any hierarchical encoder, including HRED and HIBERT like models, by using specially designed attention masks and positional encodings. We demonstrate that Hierarchical Encoding helps achieve better natural language understanding of the contexts in transformer-based models for task-oriented dialog systems through a wide range of experiments.

Hierarchical Transformer for Task Oriented Dialog Systems

Knowledge data are massive and widespread in the real-world, which can serve as good external sources to enrich conversations. However, in knowledge-grounded conversations, current models still lack the fine-grained control over knowledge selection and integration with dialogues, which finally leads to the knowledge-irrelevant response generation problems: 1) knowledge selection merely relies on the dialogue context, ignoring the inherent knowledge transitions along with conversation flows; 2) the models often over-fit during training, resulting with incoherent response by referring to unrelated tokens from specific knowledge content in the testing phase;

Augmenting Knowledge-grounded Dialog Generation with Sequential Knowledge Transition

Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data. In this paper, we propose a slot description enhanced generative approach for zero-shot cross-domain DST. Specifically, our model first encodes dialogue context and slots with a pre-trained self-attentive encoder and generates slot values in an auto-regressive manner. In addition, we incorporate Slot Type Informed Descriptions that capture the shared information across slots to facilitate cross-domain knowledge transfer. Experimental results on the MultiWOZ dataset show that our proposed method significantly improves existing state-of-the-art results in the zero-shot cross-domain setting.

Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking

Keyphrases, that concisely summarize the high-level topics discussed in a document, can be categorized into present keyphrase which explicitly appears in the source text and absent keyphrase which does not match any contiguous subsequence but is highly semantically related to the source. Most existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. In our work, a Select-Guide-Generate (SGG) approach is proposed to deal with present and absent keyphrase generation separately with different mechanisms. Specifically, SGG is a hierarchical neural network that consists of a pointing-based selector at a low layer concentrated on present keyphrase generation, a selection-guided generator at a high layer dedicated to absent keyphrase generation, and a guider in the middle to transfer information from selector to generator. Furthermore, we extend SGG to a title generation task which indicates its extensibility in natural language generation tasks.

SGG: Learning to Select, Guide, and Generate for Keyphrase Generation

Speech Act Classification determining the communicative intent of an utterance has been investigated widely over the years as a standalone task. This holds true for discussion in any fora including social media platform such as Twitter. But the emotional state of the tweeter which has a considerable effect on the communication has not received the attention it deserves. Closely related to emotion is sentiment, and understanding of one helps understand the other. In this work, we firstly create a new multi-modal, emotion-TA ('TA' means tweet act, i.e., speech act in Twitter) dataset called EmoTA collected from open-source Twitter dataset. We propose a Dyadic Attention Mechanism (DAM) based multi-modal, adversarial multi-tasking framework for jointly optimizing the three tasks, namely Tweet Act Classification (TAC), Sentiment Analysis (SA) and Emotion Recognition (ER). Experimental results indicate that the proposed framework boosts the performance of TAC compared to its uni-modal and single task TAC variants.

Towards Sentiment and Emotion aided Multi-modal Speech Act Classification in Twitter

Relational triple extraction is a crucial task for knowledge graph construction. Existing methods mainly focused on explicit relational triples that are directly expressed, but usually suffer from ignoring implicit triples that lack explicit expressions. This will lead to serious incompleteness of the constructed knowledge graphs. Fortunately, other triples in the sentence provide supplementary information for discovering entity pairs that may have implicit relations. Also, the relation types between the implicitly connected entity pairs can be identified with relational reasoning patterns in the real world. In this paper, we propose a unified framework to jointly extract explicit and implicit relational triples. To explore entity pairs that may be implicitly connected by relations, we propose a binary pointer network to extract overlapping relational triples relevant to each word sequentially and retain the information of previously extracted triples in an external memory. To infer the relation types of implicit relational triples, we propose to introduce real-world relational reasoning patterns in our model and capture these patterns with a relation network. We conduct experiments on several benchmark datasets, and the results prove the validity of our method.

Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network

Although pre-trained big models ( e.g., BERT, ERNIE, XLNet, GPT3 etc.) have delivered top performance in Seq2seq modeling, their deployments in real-world applications are often hindered by the excessive computations and memory demand involved. For many applications, including named entity recognition (NER), matching the state-of-the-art result under budget has attracted considerable attention. Drawing power from the recent advance in knowledge distillation (KD), this work presents a novel distillation scheme to efficiently transfer the knowledge learned from big models to their more affordable counterpart. Our solution highlights the construction of surrogate labels through the k-best Viterbi algorithm to distill knowledge from the teacher model. To maximally assimilate knowledge into the student model, we propose a multi-grained distillation scheme, which integrates cross entropy involved in conditional random field (CRF) and fuzzy learning. To validate the effectiveness of our proposal, we conducted a comprehensive evaluation on five NER benchmarks, reporting cross-the-board performance gains relative to competing prior-arts. We further discuss ablation results to dissect our gains.

Multi-Grained Knowledge Distillation for Named Entity Recognition

Open relation extraction (OpenRE) aims to extract novel relation types from open-domain corpora, which plays an important role in completing the relation schemes of knowledge bases (KBs). Most OpenRE methods cast different relation types in isolation without considering their hierarchical dependency. We argue that OpenRE is inherently in close connection with relation hierarchies. To address the bidirectional connections between OpenRE and relation hierarchy, we propose the task of open hierarchical relation extraction and present a novel OHRE framework for the task. To effectively integrate hierarchy information into relation representations for better novel relation extraction, we propose a dynamic hierarchical triplet objective and hierarchical curriculum training paradigm. We also present a top-down hierarchy expansion algorithm to add the extracted relations into existing hierarchies with reasonable interpretability. Comprehensive experiments show that OHRE outperforms state-of-the-art models by a large margin on both relation clustering and hierarchy expansion. The source code and experiment details of this paper can be obtained from https://github.com/thunlp/OHRE.

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