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Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., &quot;I&#39;m mad at you&quot;), humans can reason about the varying shades of contradictory statements ranging from straightforward negations (&quot;I&#39;m not mad at you&quot;) to commonsense contradictions (&quot;I&#39;m happy&quot;). Moreover, these negated or contradictory statements shift the commonsense implications of the original premise in interesting and nontrivial ways. For example, while &quot;I&#39;m mad&quot; implies &quot;I&#39;m unhappy about something,&quot; negating the premise does not necessarily negate the corresponding commonsense implications.

NAACL 2021

&quot;I&#39;m Not Mad&quot;: Commonsense Implications of Negation and Contradiction

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., "I'm mad at you"), humans can reason about the varying shades of contradictory statements ranging from straightforward negations ("I'm not mad at you") to commonsense contradictions ("I'm happy"). Moreover, these negated or contradictory statements shift the commonsense implications of the original premise in interesting and nontrivial ways. For example, while "I'm mad" implies "I'm unhappy about something," negating the premise does not necessarily negate the corresponding commonsense implications.

"I'm Not Mad": Commonsense Implications of Negation and Contradiction

technical paper

Many recent approaches towards neural information retrieval mitigate their computational costs by using a multi-stage ranking pipeline. In the first stage, a number of potentially relevant candidates are retrieved using an efficient retrieval model such as BM25. Although BM25 has proven decent performance as a first-stage ranker, it tends to miss relevant passages. In this context we propose CoRT, a simple neural first-stage ranking model that leverages contextual representations from pretrained language models such as BERT to complement term-based ranking functions while causing no significant delay at query time. Using the MS MARCO dataset, we show that CoRT significantly increases the candidate recall by complementing BM25 with missing candidates. Consequently, we find subsequent re-rankers achieve superior results with less candidates. We further demonstrate that passage retrieval using CoRT can be realized with surprisingly low latencies.

CoRT: Complementary Rankings from Transformers

Generating metaphors is a challenging task as it requires a proper understanding of abstract concepts, making connections between unrelated concepts, and deviating from the literal meaning. In this paper, we aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. Based on a theoretically-grounded connection between metaphors and symbols, we propose a method

MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding

In most cases, the lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task. In this paper, we explore training algorithms that instead optimize reward functions that explicitly consider different aspects of the style-transferred outputs. In particular, we leverage semantic similarity metrics originally used for fine-tuning neural machine translation models to explicitly assess the preservation of content between system outputs and input texts. We also investigate the potential weaknesses of the existing automatic metrics and propose efficient strategies of using these metrics for training. The experimental results show that our model provides significant gains in both automatic and human evaluation over strong baselines, indicating the effectiveness of our proposed methods and training strategies.

On Learning Text Style Transfer with Direct Rewards

Document grounded generation is the task of using the information provided in a document to improve text generation. This work focuses on two different document grounded generation tasks: Wikipedia Update Generation task and Dialogue response generation. Our work introduces two novel adaptations of large scale pre-trained encoder-decoder models focusing on building context driven representation of the document and enabling specific attention to the information in the document. Additionally, we provide a stronger BART baseline for these tasks. Our proposed techniques outperform existing methods on both automated (at least 48% increase in BLEU-4 points) and human evaluation for closeness to reference and relevance to the document. Furthermore, we perform comprehensive manual inspection of the generated output and categorize errors to provide insights into future directions in modeling these tasks

Focused Attention Improves Document-Grounded Generation

Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn to follow the underlying constraints reliably, even when supervised with large amounts of task-specific examples.

NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints

The ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity. Humans use previous experience with similar contexts to form a global view and compare it to the given context to ascertain what is missing and what is useful in the context. Inspired by this, we propose a model for clarification question generation where we first identify what is missing by taking a difference between the global and the local view and then train a model to identify what is useful and generate a question about it. Our model outperforms several baselines as judged by both automatic metrics and humans.

Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge

Large-scale language models (LMs) pretrained on massive corpora of text, such as GPT-2, are powerful open-domain text generators. However, as our systematic examination reveals, it is still challenging for such models to generate coherent long passages of text (e.g., 1000 tokens), especially when the models are fine-tuned to the target domain on a small corpus. Previous planning-then-generation methods also fall short of producing such long text in various domains. To overcome the limitations, we propose a simple but effective method of generating text in a progressive manner, inspired by generating images from low to high resolution. Our method first produces domain-specific content keywords and then progressively refines them into complete passages in multiple stages. The simple design allows our approach to take advantage of pretrained LMs at each stage and effectively adapt to any target domain given only a small set of examples. We conduct a comprehensive empirical study with a broad set of evaluation metrics, and show that our approach significantly improves upon the fine-tuned large LMs and various planning-then-generation methods in terms of quality and sample efficiency. Human evaluation also validates that our model generations are more coherent.

Progressive Generation of Long Text with Pretrained Language Models

In this talk, we present SOCCER, the first information-sparse discourse state tracking dataset. It contains over 2,000 soccer matches with 135,000+ timestamped commentary, 31,000+ within-match events and other rich meta information. It expands the currently available state tracking collection that is used to be information-dense without exception, and provides the NLP community with diverse resources for distilling better state representation.

SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain

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

https://www.aclweb.org/anthology/2021.naacl-main.347/

**Abstract:**

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Identifying Medical Self-Disclosure in Online Communities

We propose MultiOpEd, an open-domain news editorial corpus that supports various tasks pertaining to the argumentation structure in news editorials, focusing on automatic perspective discovery. News editorial is a genre of persuasive text, where the argumentation structure is usually implicit. However, the arguments presented in an editorial typically center around a concise, focused thesis, which we refer to as their perspective. MultiOpEd aims at supporting the study of multiple tasks relevant to automatic perspective discovery, where a system is expected to produce a single-sentence thesis statement summarizing the arguments presented. We argue that identifying and abstracting such natural language perspectives from editorials is a crucial step toward studying the implicit argumentation structure in news editorials.

"I'm Not Mad": Commonsense Implications of Negation and Contradiction

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CoRT: Complementary Rankings from Transformers

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"I'm Not Mad": Commonsense Implications of Negation and Contradiction

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Next from NAACL 2021

CoRT: Complementary Rankings from Transformers

Similar lecture

EnSidNet: Enhanced Hybrid Siamese-Deep Network for grouping clinical trials into drug-development pathways

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads