ymoslem/acl-abstracts huggingface dataset:
A Task-Oriented Dialogue Architecture via Transformer Neural Language Models and Symbolic Injection (2021)

Recently, transformer language models have been applied to build both task- and non-task-oriented dialogue systems. Although transformers perform well on most of the NLP tasks, they perform poorly on context retrieval and symbolic reasoning. Our work aims to address this limitation by embedding the model in an operational loop that blends both natural language generation and symbolic injection. We evaluated our system on the multi-domain DSTC8 data set and reported joint goal accuracy of 75.8{\%} (ranked among the first half positions), intent accuracy of 97.4{\%} (which is higher than the reported literature), and a 15{\%} improvement for success rate compared to a baseline with no symbolic injection. These promising results suggest that transformer language models can not only generate proper system responses but also symbolic representations that can further be used to enhance the overall quality of the dialogue management as well as serving as scaffolding for complex conversational reasoning.
Domain-independent User Simulation with Transformers for Task-oriented Dialogue Systems (2021)

Dialogue policy optimisation via reinforcement learning requires a large number of training interactions, which makes learning with real users time consuming and expensive. Many set-ups therefore rely on a user simulator instead of humans. These user simulators have their own problems. While hand-coded, rule-based user simulators have been shown to be sufficient in small, simple domains, for complex domains the number of rules quickly becomes intractable. State-of-the-art data-driven user simulators, on the other hand, are still domain-dependent. This means that adaptation to each new domain requires redesigning and retraining. In this work, we propose a domain-independent transformer-based user simulator (TUS). The structure of TUS is not tied to a specific domain, enabling domain generalization and the learning of cross-domain user behaviour from data. We compare TUS with the state-of-the-art using automatic as well as human evaluations. TUS can compete with rule-based user simulators on pre-defined domains and is able to generalize to unseen domains in a zero-shot fashion.
A Practical 2-step Approach to Assist Enterprise Question-Answering Live Chat (2021)

Live chat in customer service platforms is critical for serving clients online. For multi-turn question-answering live chat, typical Question Answering systems are single-turn and focus on factoid questions; alternatively, modeling as goal-oriented dialogue limits us to narrower domains. Motivated by these challenges, we develop a new approach based on a framework from a different discipline: Community Question Answering. Specifically, we opt to divide and conquer the task into two sub-tasks: (1) Question-Question Similarity, where we gain more than 9{\%} absolute improvement in F1 over baseline; and (2) Answer Utterances Extraction, where we achieve a high F1 score of 87{\%} for this new sub-task. Further, our user engagement metrics reveal how the enterprise support representatives benefit from the 2-step approach we deployed to production.
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss (2021)

Neural models trained for next utterance generation in dialogue task learn to mimic the n-gram sequences in the training set with training objectives like negative log-likelihood (NLL) or cross-entropy. Such commonly used training objectives do not foster generating alternate responses to a context. But, the effects of minimizing an alternate training objective that fosters a model to generate alternate response and score it on semantic similarity has not been well studied. We hypothesize that a language generation model can improve on its diversity by learning to generate alternate text during training and minimizing a semantic loss as an auxiliary objective. We explore this idea on two different sized data sets on the task of next utterance generation in goal oriented dialogues. We make two observations (1) minimizing a semantic objective improved diversity in responses in the smaller data set (Frames) but only as-good-as minimizing the NLL in the larger data set (MultiWoZ) (2) large language model embeddings can be more useful as a semantic loss objective than as initialization for token embeddings.
Do Encoder Representations of Generative Dialogue Models have sufficient summary of the Information about the task ? (2021)

Predicting the next utterance in dialogue is contingent on encoding of users' input text to generate appropriate and relevant response in data-driven approaches. Although the semantic and syntactic quality of the language generated is evaluated, more often than not, the encoded representation of input is not evaluated. As the representation of the encoder is essential for predicting the appropriate response, evaluation of encoder representation is a challenging yet important problem. In this work, we showcase evaluating the text generated through human or automatic metrics is not sufficient to appropriately evaluate soundness of the language understanding of dialogue models and, to that end, propose a set of probe tasks to evaluate encoder representation of different language encoders commonly used in dialogue models. From experiments, we observe that some of the probe tasks are easier and some are harder for even sophisticated model architectures to learn. And, through experiments we observe that RNN based architectures have lower performance on automatic metrics on text generation than transformer model but perform better than the transformer model on the probe tasks indicating that RNNs might preserve task information better than the Transformers.
GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling (2021)

In transfer learning, it is imperative to achieve strong alignment between a pre-trained model and a downstream task. Prior work has done this by proposing task-specific pre-training objectives, which sacrifices the inherent scalability of the transfer learning paradigm. We instead achieve strong alignment by simultaneously modifying both the pre-trained model and the formulation of the downstream task, which is more efficient and preserves the scalability of transfer learning. We present GenSF (Generative Slot Filling), which leverages a generative pre-trained open-domain dialog model for slot filling. GenSF (1) adapts the pre-trained model by incorporating inductive biases about the task and (2) adapts the downstream task by reformulating slot filling to better leverage the pre-trained model`s capabilities. GenSF achieves state-of-the-art results on two slot filling datasets with strong gains in few-shot and zero-shot settings. We achieve a 9 F1 score improvement in zero-shot slot filling. This highlights the value of strong alignment between the pre-trained model and the downstream task.
Schema-Guided Paradigm for Zero-Shot Dialog (2021)

Developing mechanisms that flexibly adapt dialog systems to unseen tasks and domains is a major challenge in dialog research. Neural models implicitly memorize task-specific dialog policies from the training data. We posit that this implicit memorization has precluded zero-shot transfer learning. To this end, we leverage the schema-guided paradigm, wherein the task-specific dialog policy is explicitly provided to the model. We introduce the Schema Attention Model (SAM) and improved schema representations for the STAR corpus. SAM obtains significant improvement in zero-shot settings, with a +22 F1 score improvement over prior work. These results validate the feasibility of zero-shot generalizability in dialog. Ablation experiments are also presented to demonstrate the efficacy of SAM.
Coreference-Aware Dialogue Summarization (2021)

Summarizing conversations via neural approaches has been gaining research traction lately, yet it is still challenging to obtain practical solutions. Examples of such challenges include unstructured information exchange in dialogues, informal interactions between speakers, and dynamic role changes of speakers as the dialogue evolves. Many of such challenges result in complex coreference links. Therefore, in this work, we investigate different approaches to explicitly incorporate coreference information in neural abstractive dialogue summarization models to tackle the aforementioned challenges. Experimental results show that the proposed approaches achieve state-of-the-art performance, implying it is useful to utilize coreference information in dialogue summarization. Evaluation results on factual correctness suggest such coreference-aware models are better at tracing the information flow among interlocutors and associating accurate status/actions with the corresponding interlocutors and person mentions.
Weakly Supervised Extractive Summarization with Attention (2021)

Automatic summarization aims to extract important information from large amounts of textual data in order to create a shorter version of the original texts while preserving its information. Training traditional extractive summarization models relies heavily on human-engineered labels such as sentence-level annotations of summary-worthiness. However, in many use cases, such human-engineered labels do not exist and manually annotating thousands of documents for the purpose of training models may not be feasible. On the other hand, indirect signals for summarization are often available, such as agent actions for customer service dialogues, headlines for news articles, diagnosis for Electronic Health Records, etc. In this paper, we develop a general framework that generates extractive summarization as a byproduct of supervised learning tasks for indirect signals via the help of attention mechanism. We test our models on customer service dialogues and experimental results demonstrated that our models can reliably select informative sentences and words for automatic summarization.
Incremental temporal summarization in multi-party meetings (2021)

In this work, we develop a dataset for incremental temporal summarization in a multiparty dialogue. We use crowd-sourcing paradigm with a model-in-loop approach for collecting the summaries and compare the data with the expert summaries. We leverage the question generation paradigm to automatically generate questions from the dialogue, which can be used to validate the user participation and potentially also draw attention of the user towards the contents then need to summarize. We then develop several models for abstractive summary generation in the Incremental temporal scenario. We perform a detailed analysis of the results and show that including the past context into the summary generation yields better summaries.
Mitigating Topic Bias when Detecting Decisions in Dialogue (2021)

This work revisits the task of detecting decision-related utterances in multi-party dialogue. We explore performance of a traditional approach and a deep learning-based approach based on transformer language models, with the latter providing modest improvements. We then analyze topic bias in the models using topic information obtained by manual annotation. Our finding is that when detecting some types of decisions in our data, models rely more on topic specific words that decisions are about rather than on words that more generally indicate decision making. We further explore this by removing topic information from the train data. We show that this resolves the bias issues to an extent and, surprisingly, sometimes even boosts performance.
Assessing Political Prudence of Open-domain Chatbots (2021)

Politically sensitive topics are still a challenge for open-domain chatbots. However, dealing with politically sensitive content in a responsible, non-partisan, and safe behavior way is integral for these chatbots. Currently, the main approach to handling political sensitivity is by simply changing such a topic when it is detected. This is safe but evasive and results in a chatbot that is less engaging. In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence. We then conduct political prudence analysis of various chatbots and discuss their behavior from multiple angles through our automatic metric and human evaluation metrics. The testsets and codebase are released to promote research in this area.
Large-Scale Quantitative Evaluation of Dialogue Agents' Response Strategies against Offensive Users (2021)

As voice assistants and dialogue agents grow in popularity, so does the abuse they receive. We conducted a large-scale quantitative evaluation of the effectiveness of 4 response types (avoidance, why, empathetic, and counter), and 2 additional factors (using a redirect or a voluntarily provided name) that have not been tested by prior work. We measured their direct effectiveness on real users in-the-wild by the re-offense ratio, length of conversation after the initial response, and number of turns until the next re-offense. Our experiments confirm prior lab studies in showing that empathetic responses perform better than generic avoidance responses as well as counter responses. We show that dialogue agents should almost always guide offensive users to a new topic through the use of redirects and use the user`s name if provided. As compared to a baseline avoidance strategy employed by commercial agents, our best strategy is able to reduce the re-offense ratio from 92{\%} to 43{\%}.
Understanding the Semantic Space: How Word Meanings Dynamically Adapt in the Context of a Sentence (2021)

How do people understand the meaning of the word {\textquotedblleft}small{\textquotedblright} when used to describe a mosquito, a church, or a planet? While humans have a remarkable ability to form meanings by combining existing concepts, modeling this process is challenging. This paper addresses that challenge through CEREBRA (Context-dEpendent meaning REpresentations in the BRAin) neural network model. CEREBRA characterizes how word meanings dynamically adapt in the context of a sentence by decomposing sentence fMRI into words and words into embodied brain-based semantic features. It demonstrates that words in different contexts have different representations and the word meaning changes in a way that is meaningful to human subjects. CEREBRA`s context-based representations can potentially be used to make NLP applications more human-like.
LinPP: a Python-friendly algorithm for Linear Pregroup Parsing (2021)

We define a linear pregroup parser, by applying some key modifications to the minimal parser defined in (Preller, 2007). These include handling words as separate blocks, and thus respecting their syntactic role in the sentence. We prove correctness of our algorithm with respect to parsing sentences in a subclass of pregroup grammars. The algorithm was specifically designed for a seamless implementation in Python. This facilitates its integration within the DisCopy module for QNLP and vastly increases the applicability of pregroup grammars to parsing real-world text data.
A CCG-Based Version of the DisCoCat Framework (2021)

While the DisCoCat model (Coecke et al., 2010) has been proved a valuable tool for studying compositional aspects of language at the level of semantics, its strong dependency on pregroup grammars poses important restrictions: first, it prevents large-scale experimentation due to the absence of a pregroup parser; and second, it limits the expressibility of the model to context-free grammars. In this paper we solve these problems by reformulating DisCoCat as a passage from Combinatory Categorial Grammar (CCG) to a category of semantics. We start by showing that standard categorial grammars can be expressed as a biclosed category, where all rules emerge as currying/uncurrying the identity; we then proceed to model permutation-inducing rules by exploiting the symmetry of the compact closed category encoding the word meaning. We provide a proof of concept for our method, converting {\textquotedblleft}Alice in Wonderland{\textquotedblright} into DisCoCat form, a corpus that we make available to the community.
Grammar equations (2021)

Diagrammatically speaking, grammatical calculi such as pregroups provide wires between words in order to elucidate their interactions, and this enables one to verify grammatical correctness of phrases and sentences. In this paper we also provide wirings within words. This will enable us to identify grammatical constructs that we expect to be either equal or closely related. Hence, our work paves the way for a new theory of grammar, that provides novel {\textquoteleft}grammatical truths'. We give a nogo-theorem for the fact that our wirings for words make no sense for preordered monoids, the form which grammatical calculi usually take. Instead, they require diagrams {--} or equivalently, (free) monoidal categories.
On the Quantum-like Contextuality of Ambiguous Phrases (2021)

Language is contextual as meanings of words are dependent on their contexts. Contextuality is, concomitantly, a well-defined concept in quantum mechanics where it is considered a major resource for quantum computations. We investigate whether natural language exhibits any of the quantum mechanics' contextual features. We show that meaning combinations in ambiguous phrases can be modelled in the sheaf-theoretic framework for quantum contextuality, where they can become possibilistically contextual. Using the framework of Contextuality-by-Default (CbD), we explore the probabilistic variants of these and show that CbD-contextuality is also possible.
Conversational Negation using Worldly Context in Compositional Distributional Semantics (2021)

We propose a framework to model an operational conversational negation by applying worldly context (prior knowledge) to logical negation in compositional distributional semantics. Given a word, our framework can create its negation that is similar to how humans perceive negation. The framework corrects logical negation to weight meanings closer in the entailment hierarchy more than meanings further apart. The proposed framework is flexible to accommodate different choices of logical negations, compositions, and worldly context generation. In particular, we propose and motivate a new logical negation using matrix inverse. We validate the sensibility of our conversational negation framework by performing experiments, leveraging density matrices to encode graded entailment information. We conclude that the combination of subtraction negation and phaser in the basis of the negated word yields the highest Pearson correlation of 0.635 with human ratings.
Parsing conjunctions in DisCoCirc (2021)

In distributional compositional models of meaning logical words require special interpretations, that specify the way in which other words in the sentence interact with each other. So far within the DisCoCat framework, conjunctions have been implemented as merging both conjuncts into a single output, however in the new framework of DisCoCirc merging between nouns is no longer possible. We provide an account of conjunction and an interpretation for the word {\textquoteleft}and' that solves this, and moreover ensures certain intuitively similar sentences can be given the same interpretations.
Should Semantic Vector Composition be Explicit? Can it be Linear? (2021)

Vector representations have become a central element in semantic language modelling, leading to mathematical overlaps with many fields including quantum theory. Compositionality is a core goal for such representations: given representations for {\textquoteleft}wet' and {\textquoteleft}fish', how should the concept {\textquoteleft}wet fish' be represented? This position paper surveys this question from two points of view. The first considers the question of whether an explicit mathematical representation can be successful using only tools from within linear algebra, or whether other mathematical tools are needed. The second considers whether semantic vector composition should be explicitly described mathematically, or whether it can be a model-internal side-effect of training a neural network. A third and newer question is whether a compositional model can be implemented on a quantum computer. Given the fundamentally linear nature of quantum mechanics, we propose that these questions are related, and that this survey may help to highlight candidate operations for future quantum implementation.
SemEval-2021 Task 1: Lexical Complexity Prediction (2021)

This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (Shardlow et al. 2020). CompLex is an English multi-domain corpus in which words and multi-word expressions (MWEs) were annotated with respect to their complexity using a five point Likert scale. SemEval-2021 Task 1 featured two Sub-tasks: Sub-task 1 focused on single words and Sub-task 2 focused on MWEs. The competition attracted 198 teams in total, of which 54 teams submitted official runs on the test data to Sub-task 1 and 37 to Sub-task 2.
OCHADAI-KYOTO at SemEval-2021 Task 1: Enhancing Model Generalization and Robustness for Lexical Complexity Prediction (2021)

We propose an ensemble model for predicting the lexical complexity of words and multiword expressions (MWEs). The model receives as input a sentence with a target word or MWE and outputs its complexity score. Given that a key challenge with this task is the limited size of annotated data, our model relies on pretrained contextual representations from different state-of-the-art transformer-based language models (i.e., BERT and RoBERTa), and on a variety of training methods for further enhancing model generalization and robustness: multi-step fine-tuning and multi-task learning, and adversarial training. Additionally, we propose to enrich contextual representations by adding hand-crafted features during training. Our model achieved competitive results and ranked among the top-10 systems in both sub-tasks.
SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) (2021)

In this paper, we introduce the first SemEval task on Multilingual and Cross-Lingual Word-in-Context disambiguation (MCL-WiC). This task allows the largely under-investigated inherent ability of systems to discriminate between word senses within and across languages to be evaluated, dropping the requirement of a fixed sense inventory. Framed as a binary classification, our task is divided into two parts. In the multilingual sub-task, participating systems are required to determine whether two target words, each occurring in a different context within the same language, express the same meaning or not. Instead, in the cross-lingual part, systems are asked to perform the task in a cross-lingual scenario, in which the two target words and their corresponding contexts are provided in two different languages. We illustrate our task, as well as the construction of our manually-created dataset including five languages, namely Arabic, Chinese, English, French and Russian, and the results of the participating systems. Datasets and results are available at: \url{https://github.com/SapienzaNLP/mcl-wic}.