The use of crowdworkers in NLP research is growing rapidly, in tandem with the exponential increase in research production in machine learning and AI. Ethical discussion regarding the use of crowdworkers within the NLP research community is typically
confined in scope to issues related to labor conditions such as fair pay. We draw attention to the lack of ethical considerations related to the various tasks performed by workers, including labeling, evaluation, and production. We find that the Final Rule, the common ethical framework used by researchers, did not anticipate the use of online crowdsourcing platforms for data collection, resulting in gaps between the spirit and practice of human-subjects ethics in NLP research. We enumerate common scenarios where crowdworkers performing NLP tasks are at risk of harm. We thus recommend that researchers evaluate these risks by considering the three ethical principles set up by the Belmont Report. We also clarify some common misconceptions regarding the Institutional Review Board (IRB) application. We hope this paper will serve to reopen the discussion within our community regarding the ethical use of crowdworkers.
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 st
raightforward 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. In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions. We introduce ANION, a new commonsense knowledge graph with 624K if-then rules focusing on negated and contradictory events. We then present joint generative and discriminative inference models for this new resource, providing novel empirical insights on how logical negations and commonsense contradictions reshape the commonsense implications of their original premises.
In natural language generation tasks, a neural language model is used for generating a sequence of words forming a sentence. The topmost weight matrix of the language model, known as the classification layer, can be viewed as a set of vectors, each r
epresenting a target word from the target dictionary. The target word vectors, along with the rest of the model parameters, are learned and updated during training. In this paper, we analyze the properties encoded in the target vectors and question the necessity of learning these vectors. We suggest to randomly draw the target vectors and set them as fixed so that no weights updates are being made during training. We show that by excluding the vectors from the optimization, the number of parameters drastically decreases with a marginal effect on the performance. We demonstrate the effectiveness of our method in image-captioning and machine-translation.
This project is a Zionist-American update of those projects which have been developed and planned by the Colonial and Zionist departments, which were designed to separate the bright Arab world for western parts, by planting the Zionist entity in the
heart of the Arab world, and after a series of conventions and treaties
that paved the way for carrying out such as the Convention Sykes-Picot colonial in 1916 and the Balfour Declaration in 1917. Thus, what is happening in the Arab world cannot be separated from the scheme of the US-Zionist-Western European
targets to penetrate the Arab region as a whole, in order to synthesize weak statelets which would be easy to control, and thus plunder the riches and capabilities of the Arabs, and to ensure the security "of Israel." In addition, it also reached the
objectives of those countries working towards the fragmentation and occupation of the Arab world, as well as the elimination of governments and nationalist parties, thus ending the national project and the Arab system.
One of the tools, or the colonial scenarios, posed to achieve this by the owners of the American-Zionist project is to hit the kind of gender in the Arab region, whether sectarian, ethnic or national. And thus ignite sectarian wars and civil between the components of the Arab community until you return the peoples of the region to the pre-national state, which leads to spread chaos and unrest and insecurity, leading to serious implications and repercussions of the disastrous walks of life, different cultural, political, social, economic and others. What progress allows the right climate for the division and fragmentation of the Arab states towards the character of a sectarian and nationalist and sectarian and ethnic, and thus draw a new map for the Arab region to serve the interests of the colonial powers? This climate of chaos
gives justifications and arguments of the States project owners a Zionist-American intervention in the affairs of Arab countries, and the violation of their sovereignty and control over their own resources, whether oil or gas, or take advantage of its strategic location in ways to control global trade. ...
