ymoslem/acl-abstracts huggingface dataset:
Extracting and Inferring Personal Attributes from Dialogue (2022)

Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes. We introduce the tasks of extracting and inferring personal attributes from human-human dialogue, and analyze the linguistic demands of these tasks. To meet these challenges, we introduce a simple and extensible model that combines an autoregressive language model utilizing constrained attribute generation with a discriminative reranker. Our model outperforms strong baselines on extracting personal attributes as well as inferring personal attributes that are not contained verbatim in utterances and instead requires commonsense reasoning and lexical inferences, which occur frequently in everyday conversation. Finally, we demonstrate the benefit of incorporating personal attributes in social chit-chat and task-oriented dialogue settings.
From Rewriting to Remembering: Common Ground for Conversational QA Models (2022)

In conversational QA, models have to leverage information in previous turns to answer upcoming questions. Current approaches, such as Question Rewriting, struggle to extract relevant information as the conversation unwinds. We introduce the Common Ground (CG), an approach to accumulate conversational information as it emerges and select the relevant information at every turn. We show that CG offers a more efficient and human-like way to exploit conversational information compared to existing approaches, leading to improvements on Open Domain Conversational QA.
Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents (2022)

At the heart of improving conversational AI is the open problem of how to evaluate conversations. Issues with automatic metrics are well known (Liu et al., 2016), with human evaluations still considered the gold standard. Unfortunately, how to perform human evaluations is also an open problem: differing data collection methods have varying levels of human agreement and statistical sensitivity, resulting in differing amounts of human annotation hours and labor costs. In this work we compare five different crowdworker-based human evaluation methods and find that different methods are best depending on the types of models compared, with no clear winner across the board. While this highlights the open problems in the area, our analysis leads to advice of when to use which one, and possible future directions.
KG-CRuSE: Recurrent Walks over Knowledge Graph for Explainable Conversation Reasoning using Semantic Embeddings (2022)

Knowledge-grounded dialogue systems utilise external knowledge such as knowledge graphs to generate informative and appropriate responses. A crucial challenge of such systems is to select facts from a knowledge graph pertinent to the dialogue context for response generation. This fact selection can be formulated as path traversal over a knowledge graph conditioned on the dialogue context. Such paths can originate from facts mentioned in the dialogue history and terminate at the facts to be mentioned in the response. These walks, in turn, provide an explanation of the flow of the conversation. This work proposes KG-CRuSE, a simple, yet effective LSTM based decoder that utilises the semantic information in the dialogue history and the knowledge graph elements to generate such paths for effective conversation explanation. Extensive evaluations showed that our model outperforms the state-of-the-art models on the OpenDialKG dataset on multiple metrics.
Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains (2022)

Large Transformer-based natural language understanding models have achieved state-of-the-art performance in dialogue systems. However, scarce labeled data for training, the large model size, and low inference speed hinder their deployment in low-resource scenarios. Few-shot learning and knowledge distillation techniques have been introduced to reduce the need for labeled data and computational resources, respectively. However, these techniques are incompatible because few-shot learning trains models using few data, whereas, knowledge distillation requires sufficient data to train smaller, yet competitive models that run on limited computational resources. In this paper, we address the problem of distilling generalizable small models under the few-shot setting for the intent classification task. Considering in-domain and cross-domain few-shot learning scenarios, we introduce an approach for distilling small models that generalize to new intent classes and domains using only a handful of labeled examples. We conduct experiments on public intent classification benchmarks, and observe a slight performance gap between small models and large Transformer-based models. Overall, our results in both few-shot scenarios confirm the generalization ability of the small distilled models while having lower computational costs.
MTL-SLT: Multi-Task Learning for Spoken Language Tasks (2022)

Language understanding in speech-based systems has attracted extensive interest from both academic and industrial communities in recent years with the growing demand for voice-based applications. Prior works focus on independent research by the automatic speech recognition (ASR) and natural language processing (NLP) communities, or on jointly modeling the speech and NLP problems focusing on a single dataset or single NLP task. To facilitate the development of spoken language research, we introduce MTL-SLT, a multi-task learning framework for spoken language tasks. MTL-SLT takes speech as input, and outputs transcription, intent, named entities, summaries, and answers to text queries, supporting the tasks of spoken language understanding, spoken summarization and spoken question answering respectively. The proposed framework benefits from three key aspects: 1) pre-trained sub-networks of ASR model and language model; 2) multi-task learning objective to exploit shared knowledge from different tasks; 3) end-to-end training of ASR and downstream NLP task based on sequence loss. We obtain state-of-the-art results on spoken language understanding tasks such as SLURP and ATIS. Spoken summarization results are reported on a new dataset: Spoken-Gigaword.
Multimodal Conversational AI: A Survey of Datasets and Approaches (2022)

As humans, we experience the world with all our senses or modalities (sound, sight, touch, smell, and taste). We use these modalities, particularly sight and touch, to convey and interpret specific meanings. Multimodal expressions are central to conversations; a rich set of modalities amplify and often compensate for each other. A multimodal conversational AI system answers questions, fulfills tasks, and emulates human conversations by understanding and expressing itself via multiple modalities. This paper motivates, defines, and mathematically formulates the multimodal conversational research objective. We provide a taxonomy of research required to solve the objective: multimodal representation, fusion, alignment, translation, and co-learning. We survey state-of-the-art datasets and approaches for each research area and highlight their limiting assumptions. Finally, we identify multimodal co-learning as a promising direction for multimodal conversational AI research.
Open-domain Dialogue Generation: What We Can Do, Cannot Do, And Should Do Next (2022)

Human{--}computer conversation has long been an interest of artificial intelligence and natural language processing research. Recent years have seen a dramatic improvement in quality for both task-oriented and open-domain dialogue systems, and an increasing amount of research in the area. The goal of this work is threefold: (1) to provide an overview of recent advances in the field of open-domain dialogue, (2) to summarize issues related to ethics, bias, and fairness that the field has identified as well as typical errors of dialogue systems, and (3) to outline important future challenges. We hope that this work will be of interest to both new and experienced researchers in the area.
Relevance in Dialogue: Is Less More? An Empirical Comparison of Existing Metrics, and a Novel Simple Metric (2022)

In this work, we evaluate various existing dialogue relevance metrics, find strong dependency on the dataset, often with poor correlation with human scores of relevance, and propose modifications to reduce data requirements and domain sensitivity while improving correlation. Our proposed metric achieves state-of-the-art performance on the HUMOD dataset while reducing measured sensitivity to dataset by 37{\%}-66{\%}. We achieve this without fine-tuning a pretrained language model, and using only 3,750 unannotated human dialogues and a single negative example. Despite these limitations, we demonstrate competitive performance on four datasets from different domains. Our code, including our metric and experiments, is open sourced.
RetroNLU: Retrieval Augmented Task-Oriented Semantic Parsing (2022)

While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with non-parametric retrieval-based memory has a number of benefits ranging from improved accuracy to data efficiency for knowledge-focused tasks such as question answering. In this work, we apply retrieval-based modeling ideas to the challenging complex task of multi-domain task-oriented semantic parsing for conversational assistants. Our technique, RetroNLU, extends a sequence-to-sequence model architecture with a retrieval component, which is used to retrieve existing similar samples and present them as an additional context to the model. In particular, we analyze two settings, where we augment an input with (a) retrieved nearest neighbor utterances (utterance-nn), and (b) ground-truth semantic parses of nearest neighbor utterances (semparse-nn). Our technique outperforms the baseline method by 1.5{\%} absolute macro-F1, especially at the low resource setting, matching the baseline model accuracy with only 40{\%} of the complete data. Furthermore, we analyse the quality, model sensitivity, and performance of the nearest neighbor retrieval component`s for semantic parses of varied utterance complexity.
Stylistic Response Generation by Controlling Personality Traits and Intent (2022)

Personality traits influence human actions and thoughts, which is manifested in day to day conversations. Although glimpses of personality traits are observable in existing open domain conversation corpora, leveraging generic language modelling for response generation overlooks the interlocutor idiosyncrasies, resulting in non-customizable personality agnostic responses. With the motivation of enabling stylistically configurable response generators, in this paper we experiment with end-to-end mechanisms to ground neural response generators based on both (i) interlocutor Big-5 personality traits, and (ii) discourse intent as stylistic control codes. Since most of the existing large scale open domain chat corpora do not include Big-5 personality traits and discourse intent, we employ automatic annotation schemes to enrich the corpora with noisy estimates of personality and intent annotations, and further assess the impact of using such features as control codes for response generation using automatic evaluation metrics, ablation studies and human judgement. Our experiments illustrate the effectiveness of this strategy resulting in improvements to existing benchmarks. Additionally, we yield two silver standard annotated corpora with intents and personality traits annotated, which can be of use to the research community.
Toward Knowledge-Enriched Conversational Recommendation Systems (2022)

Conversational Recommendation Systems recommend items through language based interactions with users. In order to generate naturalistic conversations and effectively utilize knowledge graphs (KGs) containing background information, we propose a novel Bag-of-Entities loss, which encourages the generated utterances to mention concepts related to the item being recommended, such as the genre or director of a movie. We also propose an alignment loss to further integrate KG entities into the response generation network. Experiments on the large-scale REDIAL dataset demonstrate that the proposed system consistently outperforms state-of-the-art baselines.
Understanding and Improving the Exemplar-based Generation for Open-domain Conversation (2022)

Exemplar-based generative models for open-domain conversation produce responses based on the exemplars provided by the retriever, taking advantage of generative models and retrieval models. However, due to the one-to-many problem of the open-domain conversation, they often ignore the retrieved exemplars while generating responses or produce responses over-fitted to the retrieved exemplars. To address these advantages, we introduce a training method selecting exemplars that are semantically relevant to the gold response but lexically distanced from the gold response. In the training phase, our training method first uses the gold response instead of dialogue context as a query to select exemplars that are semantically relevant to the gold response. And then, it eliminates the exemplars that lexically resemble the gold responses to alleviate the dependency of the generative models on that exemplars. The remaining exemplars could be irrelevant to the given context since they are searched depending on the gold response. Thus, our training method further utilizes the relevance scores between the given context and the exemplars to penalize the irrelevant exemplars. Extensive experiments demonstrate that our proposed training method alleviates the drawbacks of the existing exemplar-based generative models and significantly improves the performance in terms of appropriateness and informativeness.
On Breadth Alone: Improving the Precision of Terminology Extraction Systems on Patent Corpora (2022)

Automatic Terminology Extraction (ATE) methods are a class of linguistic, statistical, machine learning or hybrid techniques for identifying terminology in a set of documents. Most modern ATE methods use a statistical measure of how important or characteristic a potential term is to a foreground corpus by using a second background corpus as a baseline. While many variables with ATE methods have been carefully evaluated and tuned in the literature, the effects of choosing a particular background corpus over another are not obvious. In this paper, we propose a methodology that allows us to adjust the relative breadth of the foreground and background corpora in patent documents by taking advantage of the Cooperative Patent Classification (CPC) scheme. Our results show that for every foreground corpus, the broadest background corpus gave the worst performance, in the worst case that difference is 17{\%}. Similarly, the least broad background corpus gave suboptimal performance in all three experiments. We also demonstrate qualitative differences between background corpora {--} narrower background corpora tend towards more technical output. We expect our results to generalize to terminology extraction for other legal and technical documents and, generally, to the foreground/background approach to ATE.
On What it Means to Pay Your Fair Share: Towards Automatically Mapping Different Conceptions of Tax Justice in Legal Research Literature (2022)

In this article, we explore the potential and challenges of applying transformer-based pre-trained language models (PLMs) and statistical methods to a particularly challenging, yet highly important and largely uncharted domain: normative discussions in tax law research. On our conviction, the role of NLP in this essentially contested territory is to make explicit implicit normative assumptions, and to foster debates across ideological divides. To this goal, we propose the first steps towards a method that automatically labels normative statements in tax law research, and that suggests the normative background of these statements. Our results are encouraging, but it is clear that there is still room for improvement.
ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US (2022)

The research field of Legal Natural Language Processing (NLP) has been very active recently, with Legal Judgment Prediction (LJP) becoming one of the most extensively studied tasks. To date, most publicly released LJP datasets originate from countries with civil law. In this work, we release, for the first time, a challenging LJP dataset focused on class action cases in the US. It is the first dataset in the common law system that focuses on the harder and more realistic task involving the complaints as input instead of the often used facts summary written by the court. Additionally, we study the difficulty of the task by collecting expert human predictions, showing that even human experts can only reach 53{\%} accuracy on this dataset. Our Longformer model clearly outperforms the human baseline (63{\%}), despite only considering the first 2,048 tokens. Furthermore, we perform a detailed error analysis and find that the Longformer model is significantly better calibrated than the human experts. Finally, we publicly release the dataset and the code used for the experiments.
Combining WordNet and Word Embeddings in Data Augmentation for Legal Texts (2022)

Creating balanced labeled textual corpora for complex tasks, like legal analysis, is a challenging and expensive process that often requires the collaboration of domain experts. To address this problem, we propose a data augmentation method based on the combination of GloVe word embeddings and the WordNet ontology. We present an example of application in the legal domain, specifically on decisions of the Court of Justice of the European Union.Our evaluation with human experts confirms that our method is more robust than the alternatives.
A Legal Approach to Hate Speech -- Operationalizing the EU`s Legal Framework against the Expression of Hatred as an NLP Task (2022)

We propose a {\textquoteleft}legal approach' to hate speech detection by operationalization of the decision as to whether a post is subject to criminal law into an NLP task. Comparing existing regulatory regimes for hate speech, we base our investigation on the European Union`s framework as it provides a widely applicable legal minimum standard. Accurately deciding whether a post is punishable or not usually requires legal education. We show that, by breaking the legal assessment down into a series of simpler sub-decisions, even laypersons can annotate consistently. Based on a newly annotated dataset, our experiments show that directly learning an automated model of punishable content is challenging. However, learning the two sub-tasks of {\textquoteleft}target group' and {\textquoteleft}targeting conduct' instead of a holistic, end-to-end approach to the legal assessment yields better results. Overall, our method also provides decisions that are more transparent than those of end-to-end models, which is a crucial point in legal decision-making.
Privacy Pitfalls of Online Service Terms and Conditions: a Hybrid Approach for Classification and Summarization (2022)

Verbose and complicated legal terminology in online service terms and conditions (T{\&}C) means that users typically don`t read these documents before accepting the terms of such unilateral service contracts. With such services becoming part of mainstream digital life, highlighting Terms of Service (ToS) clauses that impact on the collection and use of user data and privacy are important concerns. Advances in text summarization can help to create informative and concise summaries of the terms, but existing approaches geared towards news and microblogging corpora are not directly applicable to the ToS domain, which is hindered by a lack of T{\&}C-relevant resources for training and evaluation. This paper presents a ToS model, developing a hybrid extractive-classifier-abstractive pipeline that highlights the privacy and data collection/use-related sections in a ToS document and paraphrases these into concise and informative sentences. Relying on significantly less training data (4313 training pairs) than previous representative works (287,226 pairs), our model outperforms extractive baselines by at least 50{\%} in ROUGE-1 score and 54{\%} in METEOR score. The paper also contributes to existing community efforts by curating a dataset of online service T{\&}C, through a developed web scraping tool.
Abstractive Summarization of Dutch Court Verdicts Using Sequence-to-sequence Models (2022)

With the legal sector embracing digitization, the increasing availability of information has led to a need for systems that can automatically summarize legal documents. Most existing research on legal text summarization has so far focused on extractive models, which can result in awkward summaries, as sentences in legal documents can be very long and detailed. In this study, we apply two abstractive summarization models on a Dutch legal domain dataset. The results show that existing models transfer quite well across domains and languages: the ROUGE scores of our experiments are comparable to state-of-the-art studies on English news article texts. Examining one of the models showed the capability of rewriting long legal sentences to much shorter ones, using mostly vocabulary from the source document. Human evaluation shows that for both models hand-made summaries are still perceived as more relevant and readable, and automatic summaries do not always capture elements such as background, considerations and judgement. Still, generated summaries are valuable if only a keyword summary or no summary at all is present.
Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models (2022)

In the era of billion-parameter-sized Language Models (LMs), start-ups have to follow trends and adapt their technology accordingly. Nonetheless, there are open challenges since the development and deployment of large models comes with a need for high computational resources and has economical consequences. In this work, we follow the steps of the R{\&}D group of a modern legal-tech start-up and present important insights on model development and deployment. We start from ground zero by pre-training multiple domain-specific multi-lingual LMs which are a better fit to contractual and regulatory text compared to the available alternatives (XLM-R). We present benchmark results of such models in a half-public half-private legal benchmark comprising 5 downstream tasks showing the impact of larger model size. Lastly, we examine the impact of a full-scale pipeline for model compression which includes: a) Parameter Pruning, b) Knowledge Distillation, and c) Quantization: The resulting models are much more efficient without sacrificing performance at large.
Towards Cross-Domain Transferability of Text Generation Models for Legal Text (2022)

Legalese can often be filled with verbose domain-specific jargon which can make it challenging to understand and use for non-experts. Creating succinct summaries of legal documents often makes it easier for user comprehension. However, obtaining labeled data for every domain of legal text is challenging, which makes cross-domain transferability of text generation models for legal text, an important area of research. In this paper, we explore the ability of existing state-of-the-art T5 {\&} BART-based summarization models to transfer across legal domains. We leverage publicly available datasets across four domains for this task, one of which is a new resource for summarizing privacy policies, that we curate and release for academic research. Our experiments demonstrate the low cross-domain transferability of these models, while also highlighting the benefits of combining different domains. Further, we compare the effectiveness of standard metrics for this task and illustrate the vast differences in their performance.