United Arab Emirates

Interpreting the reasoning process from questions to answers poses a challenge in approaching explainable QA. A recently proposed structured reasoning format, entailment tree, manages to offer explicit logical deductions with entailment steps in a tree structure. To generate entailment trees, prior single pass sequence-to-sequence models lack visible internal decision probability, while stepwise approaches are supervised with extracted single step data and cannot model the tree as a whole. In this work, we propose RLET, a Reinforcement Learning based Entailment Tree generation framework, which is trained utilising the cumulative signals across the whole tree. RLET iteratively performs single step reasoning with sentence selection and deduction generation modules, from which the training signal is accumulated across the tree with elaborately designed aligned reward function that is consistent with the evaluation. To the best of our knowledge, we are the first to introduce RL into the entailment tree generation task. Experiments on three settings of the EntailmentBank dataset demonstrate the strength of using RL framework.

EMNLP 2022

RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees

reasoning

question answering

poster

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Reasoning over Temporal Knowledge Graphs (TKGs) aims to predict future facts based on given history. One of the key challenges for prediction is to learn the evolution of facts. Most existing works focus on exploring evolutionary information in history to obtain effective temporal embeddings for entities and relations, but they ignore the variation in evolution patterns of facts, which makes them struggle to adapt to future data with different evolution patterns. Moreover, new entities continue to emerge along with the evolution of facts over time. Since existing models highly rely on historical information to learn embeddings for entities, they perform poorly on such entities with little historical information. To tackle these issues, we propose a novel Temporal Meta-learning framework for TKG reasoning, MetaTKG for brevity. Specifically, our method regards TKG prediction as many temporal meta-tasks, and utilizes the designed Temporal Meta-learner to learn evolutionary meta-knowledge from these meta-tasks. The proposed method aims to guide the backbones to learn to adapt quickly to future data and deal with entities with little historical information by the learned meta-knowledge. Specially, in temporal meta-learner, we design a Gating Integration module to adaptively establish temporal correlations between meta-tasks. Extensive experiments on four widely-used datasets and three backbones demonstrate that our method can greatly improve the performance.

MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning

Existing pipelined task-oriented dialogue systems usually have difficulties adapting to unseen domains, whereas end-to-end systems are plagued by large-scale knowledge bases in practice. In this paper, we introduce a novel query-driven task-oriented dialogue system, namely Q-TOD. The essential information from the dialogue context is extracted into a query, which is further employed to retrieve relevant knowledge records for response generation. Firstly, as the query is in the form of natural language and not confined to the schema of the knowledge base, the issue of domain adaption is alleviated remarkably in Q-TOD. Secondly, as the query enables the decoupling of knowledge retrieval from the generation, Q-TOD gets rid of the issue of knowledge base scalability. To evaluate the effectiveness of the proposed Q-TOD, we collect query annotations for three publicly available task-oriented dialogue datasets. Comprehensive experiments verify that Q-TOD outperforms strong baselines and establishes a new state-of-the-art performance on these datasets.

Q-TOD: A Query-driven Task-oriented Dialogue System

In this paper, we introduce the task of learning unsupervised dialogue embeddings.
Trivial approaches such as combining pre-trained word or sentence embeddings and encoding through pre-trained language models (PLMs) have been shown to be feasible for this task.
However, these approaches typically ignore the conversational interactions between interlocutors, resulting in poor performance.
To address this issue, we proposed a self-guided contrastive learning approach named dial2vec.
Dial2vec considers a dialogue as an information exchange process.
It captures the interaction patterns between interlocutors and leverages them to guide the learning of the embeddings corresponding to each interlocutor.
Then the dialogue embedding is obtained by an aggregation of the embeddings from all interlocutors.
To verify our approach, we establish a comprehensive benchmark consisting of six widely-used dialogue datasets.
We consider three evaluation tasks: domain categorization, semantic relatedness, and dialogue retrieval.
Dial2vec achieves on average 8.7, 9.0, and 13.8 points absolute improvements in terms of purity, Spearman's correlation, and mean average precision (MAP) over the strongest baseline on the three tasks respectively.
Further analysis shows that dial2vec obtains informative and discriminative embeddings for both interlocutors under the guidance of the conversational interactions and achieves the best performance when aggregating them through the interlocutor-level pooling strategy.
All codes and data are publicly available at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/dial2vec.

Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings

People can acquire knowledge in an unsupervised manner by reading, and compose the knowledge to make novel combinations. In this paper, we investigate whether pretrained language models can perform compositional generalization in a realistic setting: recipe generation. We design the counterfactual recipe generation task, which asks models to modify a base recipe according to the change of an ingredient. This task requires compositional generalization at two levels: the surface level of incorporating the new ingredient into the base recipe, and the deeper level of adjusting actions related to the changing ingredient. 
We collect a large-scale recipe dataset in Chinese for models to learn culinary knowledge, and a subset of action-level fine-grained annotations for evaluation.
We finetune pretrained language models on the recipe corpus, and use unsupervised counterfactual generation methods to generate modified recipes.
Results show that existing models have difficulties in modifying the ingredients while preserving the original text style, and often miss actions that need to be adjusted. Although pretrained language models can generate fluent recipe texts, they fail to truly learn and use the culinary knowledge in a compositional way. 
Code and data are available at https://github.com/xxxiaol/counterfactual-recipe-generation.

Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario

We study the problem of profiling news media on the Web with respect to their factuality of reporting and bias. This is an important but under-studied problem related to disinformation and ``fake news'' detection, but it addresses the issue at a coarser granularity compared to looking at an individual article or an individual claim. This is useful as it allows to profile entire media outlets in advance. Unlike previous work, which has focused primarily on text (e.g.,~on the text of the articles published by the target website, or on the textual description in their social media profiles or in Wikipedia), here our main focus is on modeling the similarity between media outlets based on the overlap of their audience. This is motivated by homophily considerations, i.e.,~the tendency of people to have connections to people with similar interests, which we extend to media, hypothesizing that similar types of media would be read by similar kinds of users. In particular, we propose GREENER (GRaph nEural nEtwork for News mEdia pRofiling), a model that builds a graph of inter-media connections based on their audience overlap, and then uses graph neural networks to represent each medium. We find that such representations are quite useful for predicting the factuality and the bias of news media outlets, yielding improvements over state-of-the-art results reported on two datasets. When augmented with conventionally used representations obtained from news articles, Twitter, YouTube, Facebook, and Wikipedia, prediction accuracy is found to improve by 2.5-27 macro-F1 points for the two tasks.

GREENER: Graph Neural Networks for News Media Profiling

Temporal Knowledge Graph (TKG) reasoning has attracted increasing attention due to its enormous potential value, and the critical issue is how to model the complex temporal structure information effectively. Recent studies use the method of encoding graph snapshots into hidden vector space and then performing heuristic deductions, which perform well on the task of entity prediction. However, these approaches cannot predict when an event will occur and have the following limitations: 1) there are many facts not related to the query that can confuse the model; 2) there exists information forgetting caused by long-term evolutionary processes. To this end, we propose a Graph Hawkes Transformer (GHT) for both TKG entity prediction and time prediction tasks in the future time. In GHT, there are two variants of Transformer, which capture the instantaneous structural information and temporal evolution information, respectively, and a new relational continuous-time encoding function to facilitate feature evolution with the Hawkes process. Extensive experiments on four public datasets demonstrate its superior performance, especially on long-term evolutionary tasks.

Graph Hawkes Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs

Question answering requiring discrete reasoning, e.g., arithmetic computing, comparison, and counting, over knowledge is a challenging task.
In this paper, we propose UniRPG, a semantic-parsing-based approach advanced in interpretability and scalability, to perform Unified discrete Reasoning over heterogeneous knowledge resources, i.e., table and text, as Program Generation. 
Concretely, UniRPG consists of a neural programmer and a symbolic program executor,
where a program is the composition of a set of pre-defined general atomic and higher-order operations and arguments extracted from table and text.
First, the programmer parses a question into a program by generating operations and copying arguments, and then, the executor derives answers from table and text based on the program.
To alleviate the costly program annotation issue, we design a distant supervision approach for programmer learning, where pseudo programs are automatically constructed without annotated derivations.
Extensive experiments on the TAT-QA dataset show that UniRPG achieves tremendous improvements and enhances interpretability and scalability compared with previous state-of-the-art methods, even without derivation annotation.
Moreover, it achieves promising performance on the textual dataset DROP without derivation annotation.

UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation

This paper investigates the sentiment analysis task from a novel perspective by incorporating sentiment knowledge and eye movement into a graph architecture, aiming to draw the eye movement-based sentiment relationships for learning the sentiment expression of the context. To be specific, we first explore a linguistic probing eye movement paradigm to extract eye movement features based on the close relationship between linguistic features and the early and late processes of human reading behavior. Furthermore, to derive eye movement features with sentiment concepts, we devise a novel weighting strategy to integrate sentiment scores extracted from affective commonsense knowledge into eye movement features, called sentiment-eye movement weights. Then, the sentiment-eye movement weights are exploited to build the sentiment-eye movement guided graph (SEMGraph) model, so as to model the intricate sentiment relationships in the context. Experimental results on two sentiment analysis datasets with eye movement signals and three sentiment analysis datasets without eye movement signals show that the proposed SEMGraph achieves state-of-the-art performance, and can also be directly generalized to those sentiment analysis datasets without eye movement signals.

SEMGraph: Incorporating Sentiment Knowledge and Eye Movement into Graph Model for Sentiment Analysis

Noisy labels are ubiquitous in natural language processing (NLP) tasks. Existing work, namely learning with noisy labels in NLP, is often limited to dedicated tasks or specific training procedures, making it hard to be widely used. To address this issue, SGD noise has been explored to provide a more general way to alleviate the effect of noisy labels by involving benign noise in the process of stochastic gradient descent. 
However, previous studies exert identical perturbation for all samples, which may cause overfitting on incorrect ones or optimizing correct ones inadequately. To facilitate this, we propose a novel stochastic tailor-made gradient noise (STGN), mitigating the effect of inherent label noise by introducing tailor-made benign noise for each sample. Specifically, we investigate multiple principles to precisely and stably discriminate correct samples from incorrect ones and thus apply different intensities of perturbation to them. A detailed theoretical analysis shows that STGN has good properties, beneficial for model generalization. Experiments on three different NLP tasks demonstrate the effectiveness and versatility of STGN. Also, STGN can boost existing robust training methods.

STGN: an Implicit Regularization Method for Learning with Noisy Labels in Natural Language Processing

Text style transfer is an important task in natural language processing with broad applications. Existing models following the masking and filling scheme suffer two challenges: the word masking procedure may mistakenly remove unexpected words and the selected words in the word filling procedure may lack diversity and semantic consistency. To tackle both challenges, in this study, we propose a style transfer model, with an adversarial masking approach and a styled filling technique (AMSF). Specifically, AMSF first trains a mask predictor by adversarial training without manual configuration. Then two additional losses, i.e. an entropy maximization loss and a consistency regularization loss, are introduced in training the word filling module to guarantee the diversity and semantic consistency of the transferred texts. Experimental results and analysis on two benchmark text style transfer data sets demonstrate the effectiveness of the proposed approaches.

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RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees

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RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees

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MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning

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