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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;

NAACL 2021

Augmenting Knowledge-grounded Dialog Generation with Sequential Knowledge Transition

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

**Read the paper on the folowing link:**

https://www.aclweb.org/anthology/2021.naacl-main.430/

**Abstract:**

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Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects

Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category. This paper explores data augmentation -- a technique particularly suitable for training with limited data -- for this few-shot, highly-multiclass text classification setting. On four diverse text classification tasks, we find that common data augmentation techniques can improve the performance of triplet networks by up to 3.0% on average.

Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning

In this paper, we tackle the task of Definition Generation (DG) in Chinese, which aims at automatically generating a definition for a word. Most existing methods take the source word as an indecomposable semantic unit. However, in parataxis languages like Chinese, word meanings can be composed using the word formation process, where a word ("桃花", peach-blossom) is formed by formation components ("桃", peach; "花", flower) using a formation rule (Modifier-Head). Inspired by this process, we propose to enhance DG with word formation features. We build a formation-informed dataset and propose a model DeFT, which decomposes words into formation features, dynamically fuses different features through a gating mechanism, and generates word definitions. Experimental results show that our method is both effective and robust.

Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation

Universal Semantic Tagging aims to provide lightweight unified analysis for all languages at the word level. Though the proposed annotation scheme is conceptually promising, the feasibility is only examined in four Indo--European languages. This paper is concerned with extending the annotation scheme to handle Mandarin Chinese and empirically study the plausibility of unifying meaning representations for multiple languages. We discuss a set of language-specific semantic phenomena, propose new annotation specifications, and build a richly annotated corpus.

Universal Semantic Tagging for English and Mandarin Chinese

Given a database schema, Text-to-SQL aims to translate a natural language question into the corresponding SQL query. Under the setup of cross-domain, traditional semantic parsing models struggle to adapt to unseen database schemas. To improve the model generalization capability for rare and unseen schemas, we propose a new architecture, ShadowGNN, which processes schemas at abstract and semantic levels. By ignoring names of semantic items in databases, abstract schemas are exploited in a well-designed graph projection neural network to obtain delexicalized representation of question and schema. Based on the domain-independent representations, a relation-aware transformer is utilized to further extract logical linking between question and schema. Finally, a SQL decoder with context-free grammar is applied. On the challenging Text-to-SQL benchmark Spider, empirical results show that ShadowGNN outperforms state-of-the-art models. When the annotated data is extremely limited (only 10\% training set), ShadowGNN gets over absolute 5\% performance gain, which shows its powerful generalization ability. Our implementation will be open-sourced at \url{https://github.com/WowCZ/shadowgnn}.

ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser

Abstract Meaning Representation parsing is a sentence-to-graph prediction task where target nodes are not explicitly aligned to sentence tokens. However, since graph nodes are semantically based on one or more sentence tokens, implicit alignments can be derived. Transition-based parsers operate over the sentence from left to right, capturing this inductive bias via alignments at the cost of limited expressiveness. In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments. We model the transitions as well as the pointer mechanism through straightforward modifications within a single Transformer architecture. Parser state and graph structure information are efficiently encoded using attention heads. We show that our action-pointer approach leads to increased expressiveness and attains large gains (+1.6 points) against the best transition-based AMR parser in very similar conditions. While using no graph re-categorization, our single model yields the second best Smatch score on AMR 2.0 (81.8), which is further improved to 83.4 with silver data and ensemble decoding.

AMR Parsing with Action-Pointer Transformer

**Read the paper on the folowing link:**

https://www.aclweb.org/anthology/2021.naacl-main.444/

**Abstract:**



NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction

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.

Augmenting Knowledge-grounded Dialog Generation with Sequential Knowledge Transition

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