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Script reasoning infers subsequent events from a given event chain, which involves the ability to understand relations between events. A human-labeled script reasoning dataset is usually of small size with limited event relations, which highlights the necessity to leverage external eventuality knowledge graphs (KG) consisting of numerous triple facts to describe the inferential relation between events. Existing methods adopt a &quot;retrieval and integration&quot; paradigm to focus merely on the graph triples that have event overlap with a script, but ignore much more supportive triples in the KG with similar inferential patterns, leading to under-exploiting. To fully exploit the KG, we propose a knowledge model to learn the inferential relations between events from the whole eventuality KG and then support downstream models by directly capturing the relation between events in a script. We further present a neural script adapter to extend the knowledge model for inferring the associated relations between an event chain and a subsequent event candidate. We evaluate the proposed approach on a popular multi-choice narrative cloze task for script reasoning and achieve new state-of-the-art accuracy, compared with baselines either incorporating external KG or not.

ACL-IJCNLP 2021

Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning

eventuality knowledge graph

script reasoning

knowledge graph

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Script reasoning infers subsequent events from a given event chain, which involves the ability to understand relations between events. A human-labeled script reasoning dataset is usually of small size with limited event relations, which highlights the necessity to leverage external eventuality knowledge graphs (KG) consisting of numerous triple facts to describe the inferential relation between events. Existing methods adopt a "retrieval and integration" paradigm to focus merely on the graph triples that have event overlap with a script, but ignore much more supportive triples in the KG with similar inferential patterns, leading to under-exploiting. To fully exploit the KG, we propose a knowledge model to learn the inferential relations between events from the whole eventuality KG and then support downstream models by directly capturing the relation between events in a script. We further present a neural script adapter to extend the knowledge model for inferring the associated relations between an event chain and a subsequent event candidate. We evaluate the proposed approach on a popular multi-choice narrative cloze task for script reasoning and achieve new state-of-the-art accuracy, compared with baselines either incorporating external KG or not.

technical paper

With the explosion of chatbot applications, Conversational Question Answering (CQA) has generated a lot of interest in recent years. Among proposals, reading comprehension models which take advantage of the conversation history (previous QA) seem to answer better than those which only consider the current question. Nevertheless, we note that the CQA evaluation protocol has a major limitation. In particular, models are allowed, at each turn of the conversation, to access the ground truth answers of the previous turns. Not only does this severely prevent their applications in fully autonomous chatbots, it also leads to unsuspected biases in their behavior. In this paper, we highlight this effect and propose new tools for evaluation and training in order to guard against the noted issues. The new results that we bring come to reinforce methods of the current state of the art.

Towards a more Robust Evaluation for Conversational Question Answering

Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use. In this work, we propose VAULT: a light-weight and parallel-efficient paragraph representation for MRC based on contextualized representation from long document input, trained using a new Gaussian distribution-based objective that pays close attention to the partially correct instances that are close to the ground-truth. We validate our VAULT architecture showing experimental results on two benchmark MRC datasets that require long context modeling; one Wikipedia-based (Natural Questions (NQ)) and the other on TechNotes (TechQA). VAULT can achieve comparable performance on NQ with a state-of-the-art (SOTA) complex document modeling approach while being 16 times faster, demonstrating the efficiency of our proposed model. We also demonstrate that our model can also be effectively adapted to a completely different domain -- TechQA -- with large improvement over a model fine-tuned on a previously published large PLM.

VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension

Although automated metrics are commonly used to evaluate NLG systems, they often correlate poorly with human judgements. Newer metrics such as BERTScore have addressed many weaknesses in prior metrics such as BLEU and ROUGE, which rely on n-gram matching. These newer methods, however, are still limited in that they do not consider the generation context, so they cannot properly reward generated text that is correct but deviates from the given reference. 
In this paper, we propose Language Model Augmented Relevance Score (MARS), a new context-aware metric for NLG evaluation. MARS leverages off-the-shelf language models, guided by reinforcement learning, to create augmented references that consider both the generation context and available human references, which are then used as additional references to score generated text. Compared with seven existing metrics in three common NLG tasks, MARS not only achieves higher correlation with human reference judgements, but also differentiates well-formed candidates from adversarial samples to a larger degree.

Language Model Augmented Relevance Score

While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions. We present Polyjuice, a general-purpose counterfactual generator that allows for control over perturbation types and locations, trained by finetuning GPT-2 on multiple datasets of paired sentences. We show that Polyjuice produces diverse sets of realistic counterfactuals, which in turn are useful in various distinct applications: improving training and evaluation on three different tasks (with around 70% less annotation effort than manual generation), augmenting state-of-the-art explanation techniques, and supporting systematic counterfactual error analysis by revealing behaviors easily missed by human experts.

Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models

Identifying relations from dialogues is more challenging than traditional sentence-level relation extraction (RE), since the difficulties of speaker information representation and the long-range semantic reasoning. Despite the successful efforts, existing methods do not fully consider the particularity of dialogues, making them difficult to truly understand the semantics between conversational arguments. In this paper, we propose two beneficial tasks, speaker prediction and trigger words prediction, to enhance the extraction of dialogue-based relations. Specifically, speaker prediction captures the characteristics of speaker-related entities, and the trigger words prediction provides supportive contexts for relations between arguments. Extensive experiments on the DialogRE dataset show noticeable improvements compared to the baseline models, which achieves a new state-of-the-art performance with a 65.5% of F1 score and a 60.5% of F1c score, respectively.

Enhancing Dialogue-based Relation Extraction by Speaker and Trigger Words Prediction

We present an efficient training approach to text retrieval with dense representations that applies knowledge distillation using the Col- BERT late-interaction ranking model. Specif- ically, we propose to transfer the knowledge from a bi-encoder teacher to a student by distilling knowledge from ColBERT’s expres- sive MaxSim operator into a simple dot product. The advantage of the bi-encoder teacher– student setup is that we can efficiently add in-batch negatives during knowledge distillation, enabling richer interactions between teacher and student models. In addition, using ColBERT as the teacher reduces training cost com- pared to a full cross-encoder. Experiments on the MS MARCO passage and document rank- ing tasks and data from the TREC 2019 Deep Learning Track demonstrate that our approach helps models learn robust representations for dense retrieval effectively and efficiently.

In-Batch Negatives for Knowledge Distillation with Tightly-Coupled Teachers for Dense Retrieval

Word Embedding maps words to vectors of real numbers. It is derived from a large corpus and is known to capture semantic knowledge from the corpus. Word Embedding is a critical component of many state-of-the-art Deep Learning techniques. However, generating good Word Embeddings is a special challenge for low-resource languages such as Nepali due to the unavailability of large text corpus. In this paper, we present NPVec1 which consists of 25 state-of-art Word Embeddings for Nepali that we have derived from a large corpus using GloVe, Word2Vec, fastText, and BERT. We further provide intrinsic and extrinsic evaluations of these Embeddings using well established metrics and methods. These models are trained using 279 million word tokens and are the largest Embeddings ever trained for Nepali language. Furthermore, we have made these Embeddings publicly available to accelerate the development of Natural Language Processing (NLP) applications in Nepali.

NPVec1: Word Embeddings for Nepali - Construction and Evaluation

Static word vectors remain important in applications where word meaning has to be modelled in the absence of context. This paper attampt to learn high-quality static word vectors by utilizing contextualized language models (CLMs).

Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection

We investigate how sentence-level transformers can be modified into effective sequence labelers at the token level without any direct supervision. Existing approaches to zero-shot sequence labeling do not perform well when applied on transformer-based architectures. As transformers contain multiple layers of multi-head self-attention, information in the sentence gets distributed between many tokens, negatively affecting zero-shot token-level performance. We find that a soft attention module which explicitly encourages sharpness of attention weights can significantly outperform existing methods.

Zero-shot Sequence Labeling for Transformer-based Sentence Classifiers

Deep neural networks (DNNs) have shown much empirical success in solving perceptual tasks across various cognitive modalities. While they are only loosely inspired by the biological brain, recent studies report considerable similarities between representations extracted from task-optimized DNNs and neural populations in the brain. DNNs have subsequently become a popular model class to infer computational principles underlying complex cognitive functions, and in turn, they have also emerged as a natural testbed for applying methods originally developed to probe information in neural populations. In this work, we utilize mean-field theoretic manifold analysis, a recent technique from computational neuroscience that connects geometry of feature representations with linear separability of classes, to analyze language representations from large-scale contextual embedding models. We explore representations from different model families (BERT, RoBERTa, GPT, etc.) and find evidence for emergence of linguistic manifolds across layer depth (e.g., manifolds for part-of-speech tags), especially in ambiguous data (i.e, words with multiple part-of-speech tags, or part-of-speech classes including many words). In addition, we find that the emergence of linear separability in these manifolds is driven by a combined reduction of manifolds’ radius, dimensionality and inter-manifold correlations.

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